The Protection Gap: Infection prevention and control Adherence and Determinants among SARS-CoV-2 Infected Healthcare Workers in Ethiopia and Implications for Future High-Consequence Pathogen Outbreaks

The Protection Gap: Infection prevention and control Adherence and Determinants among SARS-CoV-2 Infected Healthcare Workers in Ethiopia and Implications for Future High-Consequence Pathogen Outbreaks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Protection Gap: Infection prevention and control Adherence and Determinants among SARS-CoV-2 Infected Healthcare Workers in Ethiopia and Implications for Future High-Consequence Pathogen Outbreaks Zekarias Amdemariam, Tewodros Eshete, Ruth Woldeyohannes Yirgu, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9297921/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background HCW (Health care workers) infections during outbreaks of high-consequence pathogens, from SARS-CoV-2 to Ethiopia's 2025–2026 Marburg outbreak, highlight persistent Infection Prevention and Control (IPC) gaps. Understanding the settings and determinants of adherence failure is essential to prepare for future outbreaks. Methods This cross-sectional study of 1,152 SARS-CoV-2-infected HCWs in Addis Ababa (March 2020–March 2021) assessed IPC adherence among HCWs with face-to-face or body fluid/surface exposure from suspected/confirmed patients within 14 days pre-diagnosis. Clinical environments categorized via adapted OSHA pyramid: High-Pathogen Designated Areas (HPDA: isolation/treatment), General High-Risk Areas (GHR: ICUs/EDs/ORs), Medium-Risk Clinical Areas (MRCA: inpatient/outpatient), Low-Risk Support Areas (LRS: administrative/non-clinical). Adherence (PPE use/hand hygiene/donning & doffing) defined as practice in >50% of interactions. Chi-square/Fisher’s exact tests and Cramér’s V assessed bivariate associations. Multivariable logistic regression identified determinants for donning/doffing (HCWs with face-to-face contact, N=742) and N95 seal checks (N=350, N95 users). Results Only 19.2% of infected HCWs worked in HPDA. Mask (85.7–97.7%) and glove (68.2–98.0%) adherence was high. Conversely, gowns, face shields/goggles, coveralls, head caps, N95, and shoe covers fell below 50%, except for face shields/goggles (52.8%) and N95 use during Aerosol Generating Procedures (AGPs) (66.5%). N95 seal‑check compliance was 34.9%; fit‑tested N95 use was 1.1%. Hand hygiene increased from 49.3% (before face‑to‑face) to 82.5% (after body fluid exposure). Per chi-square analysis, HPDA and health professionals had significantly better adherence than non-HPDA areas and support staff. For donning/doffing , odds were lower in non-HPDA areas (GHR aOR=0.12 [0.07–0.19]; MRCA aOR=0.08 [0.04–0.14]; LRS aOR=0.07 [0.03–0.17]). Adherence was higher with AGP involvement (aOR=3.61 [2.33–5.58]) and training (aOR=1.59 [1.05–2.41]), but lower among support staff (aOR=0.35 [0.14–0.88]). N95 seal-check odds were lower in non-HPDA areas (GHR aOR=0.53 [0.30–0.93]; MRCA aOR=0.33 [0.16–0.66]; LRS aOR=0.25 [0.08–0.77]). Positive predictors included communal living (aOR=2.37 [1.29–4.35]), IPC training (aOR=1.71 [1.03–2.83]), and AGP involvement (aOR=1.71 [1.05–2.80]), while age >30 (aOR=0.57 [0.34–0.98]) and stress (aOR=0.46 [0.28–0.77]) were negative determinants. Conclusions This study underscores IPC gaps among HCWs especially in non-designated areas, where most infections occurred, highlighting the need for standardized IPC across all healthcare environments. COVID-19 Cross-Sectional Studies Ethiopia Guideline Adherence Health Personnel Infection Control Occupational Exposure Personal Protective Equipment Figures Figure 1 Figure 2 Figure 3 Introduction The emergence of high-consequence respiratory pathogens, most notably SARS-CoV-2, has imposed an unprecedented burden on global health, economic, and social systems. As of late 2024, the World Health Organization reported over 776 million confirmed cases and 7 million deaths globally, with Ethiopia recording over 500,000 cases and 7,500 deaths [ 1 ] [ 2 ] [ 3 ]. The clinical spectrum of such infections varies widely, ranging from asymptomatic carriage to critical illness requiring intensive care [ 4 ] [ 5 ]. Healthcare workers (HCWs) remain at the forefront of these outbreaks, facing disproportionate infection risks that are further exacerbated in resource-limited settings characterized by understaffing and constrained supplies [ 6 ]. This vulnerability is particularly concerning in developing nations such as Ethiopia, where healthcare systems face chronic understaffing and resource constraints. The potential for infected HCWs to transmit infection in both hospital and community settings underscores the critical importance of robust infection prevention and control (IPC) measures[ 7 ]. This was recently underscored by the country's first ever outbreak of Marburg Virus Disease, which occurred between November 2025 and January 2026. Centered in the South Ethiopia and Sidama regions, the outbreak resulted in 14 confirmed cases and a high case fatality rate, with the Ministry of Health identifying infected healthcare workers among the casualties [ 8 ]. Effective IPC adherence is the primary defense against occupational acquisition of infectious diseases. This is achieved through systematic training, the provision and appropriate use of personal protective equipment (PPE), and continuous monitoring and evaluation of safety protocols [ 9 ] [ 10 ]. However, evidence suggests that IPC adherence is often inconsistent across different clinical environments [ 11 ]. While specialized isolation units often maintain high standards, general clinical areas may experience a "risk perception gap" that compromises safety [ 12 ]. To address this, there is a need for a standardized, task-based evaluation of safety breaches across the entire healthcare facility [ 13 ] [ 14 ]. Therefore, this research aims to analyze IPC practices of HCWs with laboratory-confirmed infections who had direct face‑to‑face contact, were involved in aerosol‑generating procedures (AGPs), or experienced exposure to body fluids, materials, or surfaces of suspected or confirmed COVID‑19 patients, focusing on the critical 14-day window preceding their diagnosis. Utilizing a framework adapted from the OSHA Occupational Risk Pyramid, we categorized clinical environments into High-Pathogen Designated Areas (HPDA), General High-Risk Areas (GHR), Medium-Risk Clinical Areas (MRCA), and Low-Risk Support Areas (LRS). Furthermore, the study specifically assessed adherence to donning and doffing procedures and the performance of pre-use seal checks among HCWs to identify technical and behavioral gaps in respiratory protection. By identifying exactly where adherence fails, whether in specialized units or general wards, this study provides critical data for strengthening occupational safety and institutional resilience. These findings may inform scalable strategies for reinforcing protective measures against current and future health crises with similar transmission patterns. Materials and Methods Study design and setting This study employed a facility-based, comparative cross-sectional design to evaluate adherence to IPC protocols among HCWs in Addis Ababa, Ethiopia (March 2020–March 2021). By analyzing safety breaches among HCWs with laboratory-confirmed RT-PCR diagnoses, the study identifies vulnerabilities in routine occupational safety relevant to future emerging infectious threats. Study Population We identified 1,430 HCWs with confirmed SARS-CoV-2 infection through the Emergency Operating Center database. Of these, 1,152 HCWs consented to participate in structured interviews regarding their practices 14 days prior to diagnosis. Inclusion and Exclusion Criteria: Inclusion and Exclusion Criteria: Inclusion criteria : HCWs aged ≥ 18 years with laboratory-confirmed SARS-CoV-2 infection who were actively working in a healthcare facility and actively engaged in the care of suspected or confirmed COVID-19 patients during the 14 days prior to diagnosis, and who provided informed consent. Exclusion criteria : HCWs who were unable to recall the 14-day period prior to diagnosis, those on extended leave during the exposure window, those who declined participation, those not actively working in a healthcare facility, or those not actively engaged in the care of suspected or confirmed COVID-19 patients during the 14 days prior to diagnosis. Participant Flow Diagram This study defined two distinct analytical populations for multivariable analysis. First, donning and doffing adherence was assessed among the 742 HCWs who reported direct face-to-face contact with suspected or confirmed COVID-19 patients (representing 64.4% of all infected HCWs). Second, N95 pre-use seal-check adherence and fit testing were assessed among the subset of 350 HCWs from this group who reported using N95 respirators during face-to-face interactions (representing 47.2% of the face-to-face group and 30.4% of all infected HCWs). These subpopulations are clearly indicated in the respective multivariable models. HCW = healthcare worker; COVID-19 = coronavirus disease 2019; AGPs = aerosol-generating procedures; EOC = Emergency Operating Center Data Collection Tools, Procedures and Quality control measures The study used standardized and structured questionnaires for the purpose of data collection. The questionnaire was developed after relevant literature, the WHO risk assessment tool, and Internet sources were reviewed [ 15 – 18 ]. The questionnaire was pilot-tested with 30 HCWs not included in the final sample to assess clarity and comprehensibility. The data were collected via a paper-based questionnaire, which was filled by trained health care professionals by interviewing each study participant. The questionnaire assessed participants' compliance with standard IPC practices, PPE utilization, and hand hygiene protocols during the 14 days preceding their COVID-19 diagnosis. Supervisors conducted daily evaluations of completed questionnaires to ensure completeness, accuracy, and adherence to standardized procedures. Operational Definitions Healthcare Worker Categories In this study, we classified HCWs into supporting staff and health care professionals. The supporting staff comprises of admission and reception clerks, patient transporters, catering personnel, cleaners, ambulance drivers, security guards, administrative workers, and morgue professionals. In contrast, health care professionals include medical doctors, nurses, health officers, anesthetists, laboratory personnel, dentists, midwives, and pharmacists. Risk classification based on COVID-19 exposure We adapted the OSHA Occupational Risk Pyramid for Infectious Diseases [ 13 ] to categorize clinical environments based on the potential for contact with infectious sources. HCWs were classified into four distinct areas based on their exposure risk. High-Pathogen Designated Areas (HPDA) : Healthcare settings specifically involved in the isolation and treatment of confirmed or suspected infectious cases. This includes designated treatment centers, isolation areas, fever clinics, and medical transport operators moving known cases. General High-Risk Areas (GHR) : Areas with high potential for exposure during specialized medical, postmortem, or laboratory procedures, particularly environments where aerosol-generating procedures (AGPs) are frequently performed, such as intensive care units, emergency rooms, and operating theaters. Medium-Risk Clinical Areas (MRCA) : Settings involving frequent or close contact with the general patient population who are not confirmed cases. This includes roles in general inpatient and outpatient departments, labor wards, laboratories, and pharmacies. Low-Risk Support Areas (LRS) : Roles involving minimal patient interaction or primarily administrative functions, such as administrative offices, laundry facilities, and support staff operating in non-clinical zones. Adherence Assessment For each PPE item, hand hygiene action, and donning/doffing procedure, adherence was defined as correct practice in > 50% of relevant interactions during the 14‑day period prior to diagnosis. This threshold was chosen to pragmatically distinguish HCWs who performed a given IPC measure more often than not, recognizing that 100% adherence is rarely achievable in resource‑limited settings. Specific IPC Measures Adherence was defined as utilization of a specified PPE item for more than 50% of relevant interactions during the 14-day period prior to diagnosis. The study evaluated whether N95 respirators were test-fitted and whether pre-use seal checks were performed. Fit testing confirms that the N95 respirator forms a tight seal on the user's face before use, as some respirators fail stipulated safety thresholds [ 19 , 20 ]. OSHA enforcement guidance recommends initial fit tests for each HCW with the same model, style, and size respirator before use [ 21 ]. CDC guidelines recommend seal checks before each use following annual fit testing [ 22 ]. Pre-use seal check A pre-use seal check is a user-performed verification that an N95 respirator forms an adequate facial seal before each entry into a patient care area. A seal check is performed each time a respirator is used to ensure a facial seal that minimizes particle bypass through gaps between the wearer's skin and the N95 seal [ 23 ]. This study assessed pre-use seal check practices only among participants who reported using N95 respirators during face-to-face interactions with suspected or confirmed COVID-19 patients (N = 350). Participants were asked whether they performed a seal check before entering the clinical environment. Donning Donning is defined as appropriate application and use of PPE to achieve intended protection by reducing exposure risk; doffing is the systematic removal of PPE to avoid self-contamination [ 24 ]. Data analysis procedure Data were entered using Epi Info and analyzed with Stata version 14.0 (StataCorp, College Station, TX, USA). Descriptive Analysis: Continuous variables were assessed for normality using the Shapiro-Wilk test and visual inspection of histograms. Normally distributed continuous variables are presented as means with standard deviations (SDs); non-normally distributed variables are presented as medians with interquartile ranges (IQRs). Categorical variables are summarized as frequencies and percentages. Between-group comparisons for categorical variables were conducted using Pearson's chi-square test, with Fisher's exact test applied when expected cell frequencies were less than five. To quantify the magnitude of associations, Cramér's V effect size measures were calculated, with values interpreted as: >0.10 = small effect, > 0.30 = medium effect, > 0.50 = large effect. For significant chi-square results, standardized residuals were examined, with values exceeding ± 1.96 considered statistically significant at α = 0.05, enabling identification of specific group differences contributing to overall associations. Two separate multivariable logistic regression models were constructed to identify independent predictors of: (1) pre-use N95 seal-check adherence among N95 users (N = 350), and (2) appropriate donning and doffing practices (N = 742). Variables with a P-value < 0.2 in univariable logistic regression were considered for entry into the multivariable models. For the N95 seal-check model (outcome: performing pre-use seal check, yes/no), the initial univariable analysis included comorbidity, stress level at the time of event, history of formal N95 fit testing, living status, involvement in AGPs, prior IPC training, clinical setting, age category, and years of professional service. Variables meeting the P < 0.2 threshold were entered into the multivariable model. Backward elimination was then applied to minimize the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), yielding the final parsimonious model. For the donning and doffing model (outcome: following recommended procedures for > 50% of interactions, yes/no), the initial univariable analysis included profession, clinical setting, age category, gender, prior IPC training, years of professional service, marital status, stress level at the time of event, history of substance abuse, and average working hours per day. Variables meeting the P < 0.2 threshold were entered into the multivariable model, followed by backward elimination to minimize AIC and BIC. Model diagnostics, including the Hosmer-Lemeshow goodness-of-fit test, area under the receiver operating characteristic curve (ROC-AUC), and variance inflation factor (VIF), confirmed appropriate model fit, good discriminatory ability, and no significant multicollinearity for both final models. Statistical significance was set at P < 0.05 (two-tailed). Results from multivariable models are presented as adjusted odds ratios (AOR) with 95% confidence intervals (CI). Ethical considerations This study was approved by the Ethical Review Committee of the Addis Ababa Health Bureau (5204/227). Permission was obtained from the Addis Ababa Health Bureau, the healthcare facilities where infected HCWs were employed, and the Addis Ababa Emergency Operating Center. Written informed consent was obtained from all participants. Throughout data collection, confidentiality was protected. Participation was voluntary, and participants could withdraw at any time, even after the interview had begun, and could decline to answer any question. All methods were implemented in accordance with relevant guidelines and regulations. Results A total of 1,152 HCWs with laboratory-confirmed COVID-19 were included. The median age of participants was 29 years (IQR: 27–34; range: 19–88). The median duration of professional experience was 5 years (IQR: 3–8), and the median monthly income was 8,017 Ethiopian Birr. A significant majority (80.8%) of participants worked in areas not specifically designated for COVID-19 care prior to diagnosis. Based on the adapted OSHA Occupational Risk Pyramid, the distribution of work locations in the 14 days prior to diagnosis was: GHR – 349 (30.3%), MRCA – 400 (34.7%), LRS – 182 (15.8%), and HPDA – 221 (19.2%). Among infected participants, 1,048 (91.0%) were healthcare professionals, while the remainder were supporting staff. Among healthcare professionals, 412 (35.8%) were doctors and 434 (37.7%) were nurses or midwives. Notable disparities in IPC training coverage were observed by work location, ranging from 31.5% in MRCA to 57.5% in HPDA. Self-reported stress at the time of diagnosis was highest among HCWs in HPDA (62.4%) and GHR (57.6%) (Table 1 ). Table 1 Demographic and occupational characteristics of healthcare personnel infected during the study period in Addis Ababa, Ethiopia from March 2020 to March 2021 Socio demographic characteristics Total N(%)/Median with IQR Place of work before diagnosis Non-Designated Areas (N = 931) (80.8%) Designated Areas (N = 221) (19.2%) LRS (N = 182) (15.80%) MRCA (N = 400) (34.72%) GHR (N = 349) (30.3%) HPDA (N = 221) (19.2%) Age in years Median [IQR] 29.0 29[ 27 , 34 ] 32.0 [ 28 , 40 ] 29.0 [ 27 , 35 ] 29.0 [ 27 , 33 ] 28.0 [ 26 , 31 ] Year of professional experience Median [IQR] 5.0 5 [ 3 , 8 ] 6.0 [ 4 , 10 ] 5.0 [ 3 , 10 ] 5.0 [ 3 , 8 ] 4.0 4[ 2 , 7 ] Monthly income (ETB), Median [IQR] (n = 1,118) 8017 [6000, 10470] 6500 [3338, 8170] 8500 [6193, 10470] 9000 [6193, 11300] 8300 [6000, 10150] Gender Male 522 45.3% 75 41.2% 148 37.0% 176 50.4% 123 55.7% Female 630 54.7% 107 58.8% 252 63.0% 173 49.6% 98 44.3% Marital status Married 529 45.9% 100 54.9% 194 48.5% 162 46.4% 73 33.0% Single 590 51.2% 70 38.5% 197 49.3% 182 52.1% 141 63.8% Widowed/divorced/ separated 33 2.9% 12 6.6% 9 2.3% 5 1.4% 7 3.2% Profession of the health care workers (N = 1152) Doctors 412 35.8% 11 6.0% 159 39.8% 155 44.4% 87 39.4% Nurses/midwife 434 37.7% 13 7.1% 191 47.8% 137 39.3% 93 42.1% Health officers 60 5.2% 11 6.0% 25 6.3% 17 4.9% 7 3.2% Supporting staffs 104 9.0% 54 29.7% 17 4.3% 15 4.3% 18 8.1% Lab. professionals 65 5.6% 59 32.4% 0 0.0% 0 0.0% 6 2.7% Pharmacist 40 3.5% 31 17.0% 3 0.8% 1 0.3% 5 2.3% Anesthetist 23 2.0% 0 0.0% 1 0.3% 21 6.0% 1 0.5% Other healthcare professionals 14 1.2% 3 1.6% 4 1.0% 3 0.9% 4 1.8% IPC training(N = 1152) No 709 61.6% 104 57.1 274 68.5 237 67.9 94 42.5 Yes 443 38.4% 78 42.9 126 31.5 112 32.1 127 57.5 Living status (N = 1152) Alone 245 21.3% 26 14.3 86 21.5 88 25.2 45 20.4 With a family, friends or in a camp 907 78.7% 156 85.7 324 78.5 261 74.8 176 79.6 Stressed at the time of the event No 509 44.2% 93 51.1 185 46.3 148 42.4 83 37.6 Yes 643 55.2% 89 48.9 215 53.7 201 57.6 138 62.4 ETB = Ethiopian Birr; HPDA = High-Pathogen Designated Areas; GHR = General High-Risk Areas; MRCA = Medium-Risk Clinical Areas; LRS = Low-Risk Support Areas; IQR = interquartile range; IPC = infection prevention and control. HCW interactions with patients in the 14 days prior to diagnosis Among HCWs who contracted COVID-19, 64.4% (n = 742) reported direct face-to-face contact with suspected or confirmed cases, 55.0% (n = 631) handled patient materials, 42.6% (n = 491) had contact with contaminated surfaces, 26.1% (n = 301) experienced exposure to body fluids, and 17.1% (n = 197) were present during AGPs. Uncertainty about exposure was highest for contact with surrounding surfaces (9.4%), followed by handling patient materials (6.9%) and direct contact (6.9%); uncertainty was lowest for AGPs (0.4%) (Fig. 1 ). PPE Use during Direct Face-to-Face Interactions During direct face-to-face interactions, 87.5% of participants used medical masks for more than half of interactions, and 68.2% did so for gloves. Utilization declined significantly from HPDA to LRS for both mask and glove use (P < 0.001, V = 0.15 and V = 0.23, respectively). Regarding specialized respiratory protection, only 31.3% of participants used N95 respirators for more than half of interactions, with strong variation by work site (P < 0.001, V = 0.36). In HPDA settings, 74.8% used N95 respirators for more than half of interactions, significantly higher than expected (standardized residual = 9.8), compared to 13.0–26.6% in non-designated areas. Among participants who used N95 respirators during face-to-face interactions (N = 350), pre-use seal-check compliance was low overall (34.9%), ranging from 20.7% in MRCA to 48.1% in HPDA (P < 0.001, V = 0.24). Formal N95 fit testing was nearly absent, with only 1.1% of these participants reporting test-fitted respirators and none in MRCA or LRS (P = 0.328). Utilization of face shields, disposable gowns, coverall suits, head caps, and shoe covers exceeded 50% of the time in less than 25% of participants overall. Work site was significantly associated with utilization of all these PPE items (P < 0.001 for all), with moderate to large effect sizes: coverall suits (V = 0.43), face shields (V = 0.42), disposable gowns (V = 0.34), head caps (V = 0.30), and shoe covers (V = 0.34). In addition, disparities in PPE adherence were also evident by professional role. Supporting staff reported less frequent utilization of medical masks (P = 0.011, V = 0.11) and single-use gloves (P = 0.011, V = 0.16) compared to healthcare professionals. No significant differences by professional role were observed for other PPE items or for N95 seal-check and fit testing (Table 2 ). Table 2 Adherence to personal protective equipment utilization, donning and doffing procedures during direct patient interactions stratified by clinical setting and professional role in Addis Ababa, Ethiopia from March 2020 to March 2021 Exposure Modality Type of PPE PPE use Total Place of work before the diagnosis Occupation of the HCWs N (%) HPDA GHR MRCA LRS HCPs SSs % (SR) % (SR) %(SR) %(SR) %(SR) %(SR) PPE use during direct face to face contact (N = 742) Medical mask Never 49(6.6) 4.4 (-1.1) 2.3(-2.7) 9.8(1.9) 14.8(2.9) 6.3(-0.3) 10.6(1.1) =50% 649(87.5) 93.1(0.8) 91.0(0.6) 85.4(-0.4) 71.6(-1.5) 88.3(0.3) 74.5(-1.0) 𝜒2( Cramér's V ) P < 0.001 (V = 0.15) P = 0.011 (V = 0.11) Single use gloves Never 161(21.7) 10.7(-3.0) 15.2(-2.2) 26.0(1.5) 50.6(5.6) 20.0(-1.0) 46.8(3.7) =50% 506(68.2) 84.3(2.5) 75.4(1.39) 58.9(-1.8) 42.0(-2.9) 69.5(0.4) 48.9(-1.6) 𝜒2( Cramér's V ) P < 0.001 (V = 0.23) P = 0.011 (V = 0.16) N-95 Never 392(52.8) 16.4(-6.3) 56.3(0.8) 66.7(3.0) 71.6(2.3) 51.8(-0.4) 68.1(1.4) =50% 232(31.3) 74.8(9.8) 26.6(-1.4) 13.0(-5.1) 16.0(-2.5) 32.1(0.4) 19.1(-1.5) 𝜒2( Cramér's V ) P < 0.001 (V = 0.36) P = 0.087 (V = 0.08) Seal check of N-95 (N-350) No 228(65.1) 51.9(-1.9) 67.9(0.4) 79.3(1.6) 78.3(0.8) 65.4(0.1) 60.0(-0.3) Yes 122(34.9) 48.1 (2.6) 32.1(-0.5) 20.7(-2.2) 21.7(-1.1) 34.6(-0.1) 40.0(0.3) 𝜒2(Cramer's V) P < 0.001 (V = 0.24) P = 0.669 (V = 0.02) Fit test of N-95 (N-350) No 346(98.9) 97.7(-0.1) 99.1(0.0) 100.0(0.1) 100.0(0.1) 98.8(-0.0) 100.0(0.1) Yes 4(1.1) 2.3(1.2) 0.9(-0.3) 0.0(-1.0) 0.0(-0.5) 1.2(0.1) 0.0(-0.4) 𝜒2( Cramér's V ) P = 0.328 (V = 0.09) P = 1.000 (V= -0.02) Face shields/ googles Never 475(64.0) 22.0(-6.6) 70.7(1.3) 80.1(3.2) 76.5(1.4) 63.3(-0.2) 74.5(0.9) =50% 171(23.0) 69.8(12.3) 12.9(-3.39) 6.5(-5.4) 13.6(-1.8) 23.6(0.3) 14.9(-1.2) 𝜒2(Cramer's V) P < 0.001 (V = 0.42) P = 0.285 (V = 0.06) Disposable gowns Never 509(68.6) 34.0(-5.3) 73.0(0.9) 82.5(2.6) 80.2(1.3) 68.2(-0.1) 74.5(0.5) =50% 158(21.3) 57.9(10.0) 14.1(-2.51) 8.5(-4.3) 11.1(-2.0) 21.7(0.3) 14.9(-1.0) 𝜒2( Cramér's V ) P < 0.001 (V = 0.34) P = 0.585 (V = 0.04) Coverall suit Never 565(76.1) 34.0(-6.1) 83.2(1.3) 93.5(3.1) 84.0(0.81 75.8(-0.1) 80.9(0.4) =50% 124(16.7) 59.1(13.1) 7.8(-3.5) 1.2(-5.9) 8.6(-1.78) 17.3(0.4) 8.5(-1.4) 𝜒2( Cramér's V ) P < 0.001 (V = 0.43) P = 0.189 (V = 0.06) Head cap Never 470(63.3) 30.2(-5.3) 63.3(-0.0) 79.3(3.1) 80.2(1.91) 62.4(-0.3) 76.6(1.1) =50% 211(28.4) 62.3(8.0) 27.0(-0.5) 13.4(-4.4) 12.3(-2.72) 29.1(0.3) 19.1(-1.2) 𝜒2( Cramér's V ) P < 0.001 (V = 0.30) P = 0.170 (V = 0.07) Shoe covers Never 603(81.3) 48.4(-4.6) 86.7(1.0) 93.5(2.1) 91.4(1.01) 80.9(-0.1) 87.2(0.5) =50% 78(10.5) 36.5(10.1) 5.1(-2.7) 1.6(-4.3) 3.7(-1.89) 10.6(0.11) 8.5(-0.4) 𝜒2( Cramér's V ) P < 0.001 (V = 0.34) P = 0.624 (V = 0.04) Donning /doffing of PPE during direct face to face contact Never 423(57.0) 22.0(-5.8) 60.9(0.8) 75.6(3.9) 56.8(-0.2) 58.9(0.6) 29.8(-2.5) =50% 175(23.4) 64.2(10.5) 17.6(-2.0) 8.1(-5.0) 9.9(-2.5) 24.2(0.3) 14.9(-1.2) Don’t know the steps 72(9.7) 3.8(-2.4) 6.3(-1.8) 10.6(0.4) 29.6(5.7) 6.8(-2.5) 53.2(9.5) 𝜒2( Cramér's V ) P < 0.001 (V = 0.34) P < 0.001 (V = 0.38) SR= standardized residuals; High-Pathogen Designated Areas (HPDA); General High-Risk Areas (GHR) ; Medium-Risk Clinical Areas (MRCA) ;Low-Risk Support Areas (LRS); HCPs=health care professionals; SS= supporting staff. Statistical significance (P-values) and effect sizes (Cramer's V) are included to illustrate the strength of the association between workplace designation and protocol compliance. Multivariable Analysis: Predictors of N95 Pre-Use Seal-Check Adherence Multivariable logistic regression analysis was performed to identify independent predictors of N95 mask pre-use seal-check adherence among healthcare workers who used N95 respirators during face-to-face interactions. Several socio-demographic factors significantly influenced behavior. Healthcare workers in communal or shared living arrangements (including family, friends, or camps) were more than twice as likely to perform pre-use seal checks ( [AOR] = 2.37; 95% CI: 1.29–4.35). In contrast, older age (> 30 years) (AOR = 0.57; 95% CI: 0.34–0.98) and being stressed at the time of the event (AOR = 0.46; 95% CI: 0.28–0.77) were associated with significantly lower odds of adherence. Clinical exposure and institutional factors also served as strong positive predictors. Involvement in AGPs (AOR = 1.71; 95% CI: 1.05–2.80) and receipt of prior IPC training (AOR = 1.71; 95% CI: 1.03–2.83) were both associated with increased adherence. The clinical setting played a critical role, showing a clear gradient of risk. Compared to the HPDA reference group, working in GHR was associated with a 47% reduction in the likelihood of adherence (AOR = 0.53; 95% CI: 0.30–0.93). This decline was even more pronounced in MRCA, where the odds of performing seal checks dropped by 67% (AOR = 0.33; 95% CI: 0.16–0.66). The lowest level of adherence was observed in LRS, representing a 75% reduction in the odds of performing the necessary seal checks (AOR = 0.25; 95% CI: 0.08–0.77) (Fig. 2 ). Adherence to donning and doffing procedures Overall, 57.0% of participants never followed recommended donning and doffing procedures, with non-adherence highest in MRCA (75.6%) and GHR (60.9%). Only 23.4% of participants consistently followed the recommended steps for more than half of interactions; adherence was highest in HPDA) (64.2%), compared to 17.6% in GHR and 9.9% in LRS. Notably, 9.7% of participants reported being unaware of the recommended steps, with unawareness most frequent in LRS (29.6%) and MRCA (10.6%). Occupational differences were striking: while 58.9% of healthcare professionals reported never following procedures, 53.2% of supporting staff reported a lack of knowledge regarding the recommended steps. Ultimately, only 24.2% of healthcare professionals and 14.9% of supporting staff followed procedures for more than half of interactions (Table 2 ). Multivariable logistic regression identified three factors independently associated with adherence. Involvement in AGPs was the strongest predictor; participants who performed AGPs had 3.61 times the odds of adherence compared to those who did not (AOR = 3.61; 95% CI: 2.33–5.58; P < 0.001). Prior IPC training was also significantly associated with adherence, with trained participants having 59% higher odds of achieving the adherence threshold (AOR = 1.59; 95% CI: 1.05–2.41; P = 0.028). Work site was a critical determinant of adherence (P < 0.001). Using HPDA as the reference group (AOR = 1.00), participants in other clinical areas showed markedly lower compliance: GHR (AOR = 0.12; 95% CI: 0.07–0.19), MRCA (AOR = 0.08; 95% CI: 0.04–0.14), and LRS (AOR = 0.07; 95% CI: 0.03–0.17), representing 88%, 92%, and 93% lower odds of adherence, respectively (Table 3 ). Table 3 Multivariable logistic regression analysis identifying factors associated with adherence to donning and doffing procedures during direct face to face contact among infected healthcare personnel in Addis Ababa, Ethiopia from March 2020 to March 2021(N = 742) Variables Category Adherence (≥ 50% of time) cOR (95% CI) P-value AOR[95% CI] P-value Total N (%) No N (%) Yes N (%) IPC training No 455(61.3) 373(65.9) 82(46.9) 1.00 1.00 Yes 287(38.7) 194(34.2) 93(53.1) 2.18[1.55–3.08] < 0.001 1.59[1.05–2.41] < 0.05* Site of work HPDA 159(21.4) 57(10.0) 102(58.3) 1.00 1.00 . GHR 256(34.5) 211(37.2) 45(25.7) 0.12[0.075–0.19] < 0.001 0.12 [0.07–0.19] < 0.001*** MRCA 246(33.2) 226(39.9) 20(11.4) 0.05[0.03–0.09] < 0.001 0.08 [0.04–0.14] < 0.001*** LRS 81(10.9) 73(12.9) 8(4.6) 0.06[0.03–0.14] < 0.001 0.07 [0.03–0.17] < 0.001*** Involved in AGP No 545(73.4) 460(81.1) 85(48.6) 1.00 1.00 Yes 197(26.6) 107(18.9) 90(51.4) 4.6 [3.16–6.54] < 0.001 3.6[2.3–5.6] < 0.001*** *p < 0.05, **P < 0.01, ***p < 0.001 *Model diagnostics: Hosmer-Lemeshow χ² = 3.72 (P = 0.811), ROC-AUC = 0.823, mean VIF = 1.23* *P < 0.05; **P < 0.01; ** P < 0.001 AOR = adjusted odds ratio; cOR = crude odds ratio; CI = confidence interval; HPDA = High-Pathogen Designated Areas; GHR = General High-Risk Areas; MRCA = Medium-Risk Clinical Areas; LRS = Low-Risk Support Areas; IPC = infection prevention and control; AGPs = aerosol-generating procedures PPE use during aerosolizing procedures During AGPs, over 95% of participants used medical masks and single-use gloves for more than half of interactions. Approximately 66.5% used N95 respirators for more than half of the time. Other PPE items, including face shields, head caps, and disposable gowns, were used by only 40–60% of participants. Chi-square analysis identified significant associations between work site and use of all PPE items during AGPs (P < 0.05 for all), with the exception of medical masks (P = 0.096) and gloves (P = 0.153). The strongest associations were found for coverall suits (Cramér's V = 0.50), shoe covers (V = 0.44), and N95 respirators (V = 0.41). Participants in High-Pathogen Designated Areas (HPDA) demonstrated significantly higher adherence, with over 50% usage rates for coverall suits (SR = 6.3), shoe covers (SR = 5.4), N95 respirators (SR = 3.1), face shields (SR = 4.2), and disposable gowns (SR = 4.0). In contrast, GHR showed negative associations for use of PPE for more than half of the time, particularly for N95 respirators (SR = -1.8), coverall suits (SR = -4.0), face shields (SR = -2.8), disposable gowns (SR = -2.5), head caps (SR = -1.1), and shoe covers (SR = -3.0). No statistically significant associations were identified between PPE utilization and professional category during AGPs (Table 4 ). PPE use during interactions with body fluids During exposure to patient body fluids, 97.7% of participants used medical masks and 93.4% used gloves for more than half of the time. However, only 40.2% used N95 respirators for more than half of the time, with notably higher utilization in HPDA settings. Regarding additional protective equipment, many participants never used face shields (50.8%), disposable gowns (53.8%), coverall suits (65.4%), head caps (53.2%), or shoe covers (70.1%) during contact with body fluids. Non-utilization of these items decreased progressively from HPDA to non-designated areas. Utilization patterns revealed significant site-specific variations (P < 0.001 for all), with the exception of medical masks and gloves. HPDA settings exhibited the highest compliance rates (≥ 50%), while MRCA showed markedly lower utilization. Healthcare professionals demonstrated significantly higher compliance than supporting staff, particularly for medical masks (98.0% vs. 66.7%; P = 0.001, V = 0.46) and gloves (94.9% vs. 16.7%; P < 0.001, V = 0.58). The disparity was most pronounced for gloves, where adherence among supporting staff was only 16.7% compared to 94.9% among healthcare professionals. While trends suggested lower utilization of gowns, face shields, and coverall suits among supporting staff, these differences did not reach statistical significance (Table 4 ). Table 4 Adherence to personal protective equipment utilization during aerosol generating procedures and contact with body fluids stratified by professional role and clinical setting in Addis Ababa, Ethiopia from March 2020 to March 2021 Exposure Modality Type of PPE PPE use Total Place of work before the diagnosis Occupation of the HCWs N (%) HPDA GHR MRCA LRS P value (V) HCP SS P value (V) % (SR) % (SR) % (SR) % (SR) % (SR) % (SR) PPE use during AGP (n = 197) Medical mask Never 3(1.5) 2.9(0.9) 0.0(-1.2) 0.0(-0.6) 7.1(1.7) P = 0.096 V = 0.15 1.5(0.0) 0.0(-0.2) P = 1.000 V = 0.02 =50% 192(97.5) 94.2(-0.3) 100(0.3) 100.0(0.1) 92.9(-0.2) 97.4(-0.0) 100.0(0.1) Single use gloves Never 3(1.5) 1.4(-0.1) 1.1(-0.4) 0.0(-0.6) 7.1(1.7) P = 0.153 V = 0.15 1.5(0.0) 0.0(-0.2) P = 1.000 V = 0.02 =50% 193(98.0) 98.6(0.1) 97.9(0.0) 100.0(0.1) 92.9(-0.2) 97.9(0.0) 100.0(0.0) N-95 Never 51(25.9) 1.4(-4.0) 41.5(3.0) 30.0(0.4) 35.7(0.7) P < 0.001 V = 0.41 25.8(-0.0) 33.3(0.3) P = 1.000 V = 0.04 =50% 131(66.5) 97.1(3.1) 51.1(-1.8) 35.0(-1.7) 64.3(-0.1) 66.5(0.0) 66.7(0.0) Face shields/ googles Never 73(37.1) 5.8(-4.3) 54.3(2.7) 55.0(1.3) 50.0(0.8) P < 0.001 V = 0.39 36.6(-0.1) 66.7(0.8) P = 1.000 V = 0.08 =50% 104(52.8) 89.9(4.2) 31.9(-2.8) 30.0(-1.4) 42.9(-0.5) 53.1(0.1) 33.3(-0.5) Gowns Never 91(46.2) 18.8(-3.3) 60.6(2.1) 70.0(1.6) 50.0(0.2) P < 0.001 V = 0.34 45.9(-0.1) 66.7(0.5) P = 1.000 V = 0.06 =50% 86(43.7) 75.4(4.0) 26.6(-2.5) 15.0(-1.9) 42.9(-0.1) 43.8(0.0) 33.3(-0.3) Coverall suit Never 111(56.3) 15.9(-4.5) 76.6(2.6) 95.0(2.3) 64.3(0.4) P < 0.001 V = 0.50 56.2(-0.0) 66.7(0.2) P = 1.000 V = 0.04 =50% 72(36.5) 82.6(6.3) 11.7(-4.0) 0.0(-2.7) 28.6(-0.5) 36.6(0.0) 33.3(-0.1) Head cap Never 73(37.1) 11.6(-3.5) 45.7(1.4) 70.0(2.4) 57.1(1.2) P < 0.001 V = 0.30 36.6(-0.1) 66.7(0.8) P = 0.625 V = 0.08 =50% 114(57.9) 84.1(2.9) 48.9(-1.1) 25.0(-1.9) 35.7(-1.1) 58.2(0.1) 33.3(-0.6) Shoe covers Never 126(64.0) 24.6(-4.1) 81.9(2.2) 100.0(2.0) 85.7(1.0) P < 0.001 V = 0.44 63.9(-0.0) 66.7(0.1) P = 1.000 V = 0.04 =50% 53(26.9) 60.9(5.4) 10.6(-3.0) 0.0(-2.3) 7.1(-1.4) 26.8(-0.0) 33.3(0.2) PPE Use during contact with patient body fluids (n = 301) Medical mask Never 4(1.3) 2.4(0.9) 0.0(-1.2) 2.4(0.9) 0.0(-0.6) P = 0.081 V = 0.14 1.4(0.0) 0.0(-0.3) P = 0.001 V = 0.46 =50% 294(97.7) 94.0(-0.3) 100.0(0.3) 97.6(0.0) 100.0(0.1) 98.3(0.1) 66.7(-0.8) Single use gloves Never 12(4.0) 2.4(-0.7) 3.6(-0.2) 4.8(0.4) 8.7(1.1) P = 0.189 V = 0.12 2.4(-1.4) 83.3(9.7) P < 0.001 V = 0.58 =50% 281(93.4) 97.6(0.4) 93.8(0.0) 89.2(-0.4) 91.3(-0.1) 94.9(0.3) 16.7(-1.9) N-95 Never 143(47.5) 21.7(-3.4) 51.8(0.7) 66.3(2.5) 52.2(0.3) P < 0.001 V = 0.34 46.8(-0.2) 83.3(1.3) P = 0.332 V = 0.10 =50% 121(40.2) 74.7(5.0) 36.6(-0.6) 12.0(-4.1) 34.8(-0.4) 40.7(0.1) 16.7(-0.9) Face shields/ googles Never 153(50.8) 13.3(-4.8) 66.1(2.3) 69.9(2.4) 43.5(-0.5) P < 0.001 V = 0.42 50.2(-0.2) 83.3(1.1) P = 0.408 V = 0.10 =50% 111(36.9) 80.7(6.6) 23.2(-2.4) 14.5(-3.4) 26.1(-0.9) 37.3(0.1) 16.7(-0.8) Gowns Never 162(53.8) 20.5(-4.1) 67.9(2.0) 71.1(2.1) 43.5(-0.7) P < 0.001 V = 0.37 52.9(-0.2) 100.0(1.5) P = 0.088 V = 0.13 =50% 112(37.2) 75.9(5.8) 23.2(-2.4) 16.9(-3.0) 39.1(0.2) 38.0(0.2) 0.0(-1.5) Coverall suit Never 197(65.4) 20.5(-5.1) 81.3(2.1) 88.0(2.5) 69.6(0.2) P < 0.001 V = 0.46 65.1(-0.1) 83.3(0.5) P = 0.774 V = 0.06 =50% 86(28.6) 75.9(8.1) 11.6(-3.4) 6.0(-3.8) 21.7(-0.6) 28.8(0.1) 16.7(-0.6) Head cap Never 160(53.2) 16.9(-4.5) 56.3(0.5) 79.5(3.3) 73.9(1.4) P < 0.001 V = 0.35 52.5(-0.2) 83.3(1.0) P = 0.463 V = 0.09 =50% 124(41.2) 74.7(4.8) 39.3(-0.3) 16.9(-3.5) 17.4(-1.8) 41.7(0.1) 16.7(-0.9) Shoe covers Never 211(70.1) 27.7(-4.6) 85.7(2.0) 89.2(2.1) 78.3(0.5) P < 0.001 V = 0.41 69.8(-0.1) 83.3(0.4) P = 1.000 V = 0.05 =50% 65(21.6) 53.0(6.2) 8.9(-2.9) 8.4(-2.6) 17.4(-0.4) 21.7(0.0) 16.7(-0.3) SR= standardized residuals; High-Pathogen Designated Areas (HPDA); General High-Risk Areas (GHR) ; Medium-Risk Clinical Areas (MRCA) ;Low-Risk Support Areas (LRS); HCPs=health care professionals; SS= supporting staff; AGE: Aerosol Generating Procedures. Statistical significance (P-values) and effect sizes (Cramér's V) are included to illustrate the strength of the association between workplace designation and protocol compliance PPE Use during Contact with Patient Materials and Surfaces During interactions with patient materials and surrounding surfaces, medical masks were the most consistently used PPE. Adherence for mask use during material handling and surface contact was 89.1% and 85.7%, respectively, with utilization increasing significantly from LRS to HPDA (P < 0.001, V = 0.14). Single-use gloves were the second most common PPE, used by 73.5% of participants during material interactions and 63.1% during surface contact. Glove utilization in LRS was approximately 31–32%, rising sharply to over 89% in HPDA settings (P < 0.001, V = 0.27–0.28) .Specialized respiratory protection remained low across both environmental domains, with only 32.2% of participants using N95 respirators during material interactions and 38.1% during surface contact. However, utilization was consistently higher in HPDA zones (up to 80.2% ) compared to non-specialized areas (P < 0.001, V = 0.37–0.40). Other PPE items, including face shields, disposable gowns, coverall suits, head caps, and shoe covers, were used by only 10–30% of participants for more than half of interactions, with even lower adherence in non-designated areas. Chi-square analysis revealed significant associations between work site and utilization of all PPE items (P < 0.001 for all), with very strong effect sizes for coverall suits (V = 0.47–0.50), face shields (V = 0.44–0.46), and shoe covers (V = 0.37–0.42). Participants in HPDA settings demonstrated significantly higher adherence rates (≥ 50%), characterized by strong positive standardized residuals for coverall suits (SR = 13.0 for materials, 11.6 for surfaces), face shields (SR = 11.7 for materials, 10.2 for surfaces), and disposable gowns (SR = 10.1 for materials). Regarding professional categories, healthcare professionals generally reported higher PPE utilization than supporting staff. Significant associations were noted for medical masks and gloves during handling of patient materials (P = 0.028, V = 0.10 and P = 0.013, V = 0.12, respectively). Coverall suit usage during surface contact showed a significant difference by occupation (P = 0.003, Cramér's V = 0.168). No significant associations were found between professional category and the use of other PPE items during these environmental interactions (Table 5 ) . Table 5 Adherence to personal protective equipment utilization during contact with patient materials and contaminated surfaces stratified by clinical setting and professional role in Addis Ababa, Ethiopia from March 2020 to March 2021 Exposure Modality Type of PPE PPE use Total N (%) Place of work before the diagnosis of COVID 19 P value (v) Occupation HPDA GHR MRCA LRS HCPs SS P value(v) %(SR) %(SR) %(SR) %(SR) %(SR) %(SR) PPE use during contact with patient materials (n = 631) Medical mask Never 39(6.2) 2.8(-1.6) 3.2(-1.8) 8.2(1.2) 18.3(3.8) P < 0.001 V = 0.14 5.9(-0.2) 9.8(0.9) P = 0.028 V = 0.102 =50% 562(89.1) 95.0(0.8) 92.8(0.6) 87.5(-0.2) 66.7(-1.8) 89.8(0.2) 78.0(-0.8) Single use gloves Never 117(18.5) 6.4(-3.4) 13.1(-1.9) 21.6(1.0) 56.7(6.9) P < 0.001 V = 0.27 17.3(-0.7) 36.6(2.7) P = 0.013 V = 0.123 =50% 464(73.5) 89.4(2.2) 81.1(1.3) 66.8(-1.1) 31.7(-3.8) 74.6(0.3) 58.5(-1.1) N-95 Never 343(54.4) 16.3(-6.1) 55.4(0.2) 72.1(3.5) 78.3(2.5) P < 0.001 V = 0.40 53.7(-0.2) 63.4(0.8) P = 0.195 V = 0.072 =50% 203(32.2) 77.3(9.5) 28.8(-0.9) 12.5(-5.0) 6.7(-3.5) 33.1(0.4) 19.5(-1.4) Face shields/ googles Never 418(66.2) 21.3(-6.6) 74.8(1.6) 82.7(2.9) 83.3(1.6) P < 0.001 V = 0.44 66.1(0.0) 68.3(0.2) P = 0.772 V = 0.026 =50% 147(23.3) 70.9(11.7) 11.7(-3.6) 9.1(-4.2) 3.3(-3.2) 23.6(0.1) 19.5(-0.5) Gowns Never 444(70.4) 31.2(-5.5) 76.6(1.1) 86.1(2.7) 85.0(1.4) P < 0.001 V = 0.38 70.5(0.0) 68.3(-0.2) P = 0.697 V = 0.031 =50% 131(20.8) 59.6(10.1) 11.7(-3.0) 9.6(-3.5) 1.7(-3.3) 20.8(0.1) 19.5(-0.2) Coverall suit Never 488(77.3) 31.9(-6.1) 86.0(1.5) 94.7(2.9) 91.7(1.3) P < 0.001 V = 0.47 77.5(0.0) 75.6(-0.1) P = 0.092 V = 0.089 =50% 100(15.8) 59.6(13.0) 5.4(-3.9) 1.4(-5.2) 1.7(-2.8) 16.3(0.3) 9.8(-1.0) Head cap Never 404(64.0) 27.7(-5.4) 68.9(0.9) 77.4(2.4) 85.0(2.0) P < 0.001 V = 0.32 63.4(-0.2) 73.2(0.7) P = 0.508 V = 0.050 =50% 187(29.6) 65.2(7.8) 25.2(-1.2) 17.3(-3.3) 5.0(-3.5) 30.2(0.2) 22.0(-0.9) Shoe covers Never 515(81.6) 46.8(-4.6) 88.3(1.1) 94.7(2.1) 93.3(1.0) P < 0.001 V = 0.37 81.5(0.0) 82.9(0.1) P = 1.000 V = 0.010 =50% 69(10.9) 39.0(10.1) 4.5(-2.9) 1.9(-3.9) 0.0(-2.6) 11.0(0.1) 9.8(-0.2) PPE use during contact with patient surfaces around patients (n = 491) Medical mask Never 47(9.6) 4.3(-1.8) 5.7(-1.6) 11.8(0.9) 29.2(4.4) P < 0.001 V = 0.14 9.5(-0.1) 11.1(0.3) P = 0.431 V = 0.052 =50% 421(85.7) 92.2(0.8) 89.7(0.6) 83.0(-0.4) 64.6(-1.6) 86.2(0.1) 80.6(-0.3) Single use gloves Never 145(29.5) 4.3(-5.0) 26.4(-0.8) 41.2(2.7) 64.6(4.5) P < 0.001 V = 0.28 28.4(-0.5) 44.4(1.7) P = 0.136 V = 0.092 =50% 310(63.1) 91.4(3.8) 66.7(0.6) 47.7(-2.4) 31.3(-2.8) 64.2(0.3) 50.0(-1.0) N-95 Never 234(47.7) 12.1(-5.6) 50.0(0.5) 64.7(3.1) 70.8(2.3) P < 0.001 V = 0.37 47.0(-0.2) 55.6(0.7) P = 0.207 V = 0.078 =50% 187(38.1) 80.2(7.4) 33.3(-1.0) 17.6(-4.1) 18.8(-2.2) 39.1(0.4) 25.0(-1.3) Face shields Never 316(64.4) 15.5(-6.6) 76.4(2.0) 82.4(2.8) 81.3(1.5) P < 0.001 V = 0.46 64.2(-0.1) 66.7(0.2) P = 0.300 V = 0.071 =50% 123(25.1) 72.4(10.2) 12.1(-3.4) 9.2(-3.9) 8.3(-2.3) 25.7(0.3) 16.7(-1.0) Gowns Never 345(70.3) 23.3(-6.0) 81.6(1.8) 88.9(2.8) 83.3(1.1) P < 0.001 V = 0.45 70.1(0.0) 72.2(0.1) P = 0.137 V = 0.091 =50% 101(20.6) 62.9(10.1) 9.8(-3.1) 5.2(-4.2) 6.3(-2.2) 21.3(0.4) 11.1(-1.3) Coverall suit Never 373(76.0) 25.9(-6.2) 89.1(2.0) 94.1(2.6) 91.7(1.3) P < 0.001 V = 0.50 76.0(0.0) 75.0(-0.1) P = 0.003 V = 0.168 =50% 87(17.7) 62.9(11.6) 5.7(-3.8) 2.0(-4.6) 2.1(-2.6) 18.7(0.5) 5.6(-1.7) Head cap Never 312(63.5) 19.0(-6.0) 71.3(1.3) 81.0(2.7) 87.5(2.1) P < 0.001 V = 0.39 62.6(-0.2) 75.0(0.9) P = 0.186 V = 0.079 =50% 145(29.5) 69.0(7.8) 24.1(-1.3) 13.1(-3.8) 6.3(-3.0) 30.5(0.4) 16.7(-1.4) Shoe covers Never 389(79.2) 37.1(-5.1) 92.5(2.0) 92.8(1.9) 89.6(0.8) P < 0.001 V = 0.42 79.1(0.0) 80.6(0.1) P = 0.275 V = 0.074 =50% 60(12.2) 41.4(9.0) 2.9(-3.5) 3.3(-3.2) 4.2(-1.6) 12.7(0.3) 5.6(-1.1) SR= standardized residuals; High-Pathogen Designated Areas (HPDA); General High-Risk Areas (GHR) ; Medium-Risk Clinical Areas (MRCA) ;Low-Risk Support Areas (LRS); HCPs=health care professionals; SS= supporting staff. Statistical significance (P-values) and effect sizes (Cramér's V) are included to illustrate the strength of the association between workplace designation and protocol compliance Hand hygiene practices of infected HCWs Before direct face-to-face patient contact, 49.3% of participants practiced hand hygiene for more than half of interactions. Adherence increased to 72.6% following direct patient contact and reached 82.5% after contact with environmental surfaces. Comparative analysis revealed a consistent increase in hand hygiene compliance from LRS to HPDA. Compliance rates for performing hand hygiene for more than half of interactions improved from 32.1% to 62.9% before direct patient contact, from 51.9% to 81.1% after direct contact, and from 60.4% to 88.8% after surface contact. After contact with patient body fluids, 87.7% of participants adhered to hand hygiene practices. These rates remained notably high across all work sites: 91.3% in LRS, 84.3% in MRCA, 89.3% in GHR, and 88.0% in HPDA. Chi-square analysis revealed significant associations between hand hygiene practices and work site (P < 0.001 for most interactions), with the notable exception of hand hygiene following contact with body fluids (P = 0.215). Participants in HPDA demonstrated the highest adherence before direct patient contact ( [SR] = 2.4). Conversely, staff in LRS were significantly more likely to report never performing hand hygiene before direct contact (SR = 3.3), after direct contact (SR = 4.8), or after exposure to body fluids (SR = 3.7) . A significant disparity in hand hygiene practices was identified between healthcare professionals and supporting staff. Before face-to-face contact, 50.6% of healthcare professionals adhered to hand hygiene protocols for more than half of interactions, compared to 29.8% of supporting staff. This gap was most pronounced after exposure to body fluids, with compliance rates of 89.2% for healthcare professionals versus 16.7% for supporting staff. Supporting staff were nearly 3.5 times more likely than professionals to never perform hand hygiene after direct contact (42.6% vs. 12.4%; SR = 5.1). Statistical analysis confirmed significant differences in compliance across all interactions (P < 0.001 for all). Supporting staff were significantly more likely to report never performing hand hygiene before contact (SR = 2.9), after contact (SR = 5.1), following exposure to body fluids (SR = 1.5), and after surface contact (SR = 2.7). Alcohol-based hand rub was the most common method used across all patient interactions (Table 6 ) . Table 6 Adherence to hand hygiene practices among healthcare personnel stratified by clinical setting and professional role in Addis Ababa, Ethiopia from March 2020 to March 2021 Hand hygiene practice Total N (%) Place of work before the diagnosis of COVID 19 Occupation HPDA GHR MRCA LRS HCPs SSs %(SR) %(SR) % (SR) % (SR) % (SR) % (SR) Hand hygiene practice before direct face to face contact Never 212 (28.6) 15.7 (-3.0) 24.6 (-1.2) 34.6 (1.8) 48.1(3.3) 27.1(-0.8) 51.1(2.9) =50% 366 (49.3) 62.9 (2.4) 52.0(0.6) 43.5 (-1.3) 32.1(-2.2) 50.6(0.5) 29.8(-1.9) 𝜒2( Cramér's V ) P < 0.001 (V = 0.16) P = 0.002 (V = 0.13) Material used during hand hygiene practice before direct face to face contact Alcohol 449 (84.7) 85.8 (0.1) 81.3(-0.5) 88.2 (0.5) 83.3(-0.1) 85.0(0.1) 78.3(-0.3) Soap and water 77 (14.5) 14.2 (-0.1) 18.1(1.3) 10.6(-1.3) 14.3(-0.0) 14.2(-0.19) 21.7(0.9) Water 4 (0.8) 0.0(-1.0) 0.5(-0.4) 1.2(0.7) 2.4(1.2) 0.8(0.1) 0.0(-0.4) 𝜒2( Cramér's V ) P = 0.231 (V = 0.08) P = 0.465 (V = 0.046) Hand hygiene practice after direct face to face contact Never 106 (14.3) 11.3 (-1.0) 8.6 (-2.4) 15.4(0.5) 34.6(4.8) 12.4(-1.3) 42.6(5.1) =50% 539 (72.6) 81.1(1.3) 76.6 (0.7) 69.9(-0.5) 51.9(-2.2) 74.5(0.6) 44.7(-2.3) 𝜒2( Cramér's V ) P < 0.001 (V = 0.15) P < 0.001 (V = 0.21) Material used during hand hygiene practice after direct face to face contact Alcohol 485 (76.5) 74.3(-0.3) 78.2 (0.3) 76.8(0.1) 73.6(-0.2) 76.8(0.1) 70.4(-0.3) Soap and water 146 (23.0) 25.7(0.7) 21.8 (-0.4) 22.2(-0.2) 24.5(0.2) 22.7(-0.2) 29.6(0.9) Water 3 (0.5) 0.0(-0.8) 0.0 (-1.1) 1.0(1.0) 1.9(1.5) 0.5(0.1) 0.0(-0.4) 𝜒2( Cramér's V ) P = 0.421(V = 0.07) P = 0.465(V = 0.05) Hand hygiene practice after contact with body fluids Never 13 (4.3) 2.4(-0.8) 1.8 (-1.3) 9.6(2.3) 4.3(0.0) 4.1(-0.2) 16.7(1.5) =50% 264 (87.7) 88.0(0.0) 89.3(0.2) 84.3(-0.3) 91.3(0.2) 89.2(0.3) 16.7(-1.9) 𝜒2( Cramér's V ) P = 0.215 (V = 0.12) P < 0.001 (V = 0.33) Material used during hand hygiene practice after contact with body fluids Alcohol 206 (71.5) 69.1(-0.3) 70.9(-0.1) 73.3(0.2) 77.3(0.3) 71.4(-0.0) 80.0(0.2) Soap and water 82 (28.5) 30.9(0.4) 29.1(0.1) 26.7(-0.3) 22.7(-0.5) 28.6(0.0) 20.0(-0.4) 𝜒2( Cramér's V ) P = 0.867 (V = 0.05) P = 1.000 (V= -0.03) Hand hygiene practice after contact with surfaces No 86(17.5) 11.2(-1.6) 12.6(-1.5) 20.9(1.0) 39.6(3.7) 16.0(-0.8) 36.1(2.7) Yes 405 (82.5 88.8(0.8) 87.4(0.7) 79.1(-0.5) 60.4(-1.7) 84.0(0.4) 63.9(-1.2) 𝜒2( Cramér's V ) P < 0.001 (V = 0.22) P < 0.001 (V =-0.14) Material used during hand hygiene practice after contact with surfaces Alcohol 323 (79.8) 73.8(-0.7) 83.6(0.5) 84.3(0.6) 62.1(-1.1) 79.8(0.0) 78.3(-0.1) Soap and water 82 (20.2) 26.2(1.4) 16.4(-1.0) 15.7(-1.1) 37.9(2.1) 20.2(-0.0) 21.7(0.2) 𝜒2( Cramér's V ) P = 0.013 (V = 0.16) P = 0.793 (V = 0.01) SR= standardized residuals; High-Pathogen Designated Areas (HPDA); General High-Risk Areas (GHR) ; Medium-Risk Clinical Areas (MRCA) ;Low-Risk Support Areas (LRS); HCPs=health care professionals; SS= supporting staff. Statistical significance (P-values) and effect sizes (Cramer's V) are included to illustrate the strength of the association between workplace designation and protocol compliance Discussion This study evaluated IPC adherence among COVID-19–infected HCWs during the 14 days preceding their diagnosis. Key findings included suboptimal use of fluid-resistant PPE, reactive hand hygiene patterns (higher after contact than before), and significant gaps in N95 seal-checking, fit testing, and donning/doffing procedures. A consistent gradient of adherence was observed, with superior practices in HPDA compared to non-designated areas, and among healthcare professionals compared to supporting staff. After multivariable logistic regression, among HCWs with direct face-to-face contact (N = 742), donning and doffing adherence was significantly associated with work site, IPC training, professional category, and involvement in AGPs. In contrast, among those who used N95 respirators during face-to-face contact (N = 350), pre-use seal-check adherence was associated with work site, age (>30 years), stress at the time of event, communal living, IPC training, and involvement in AGPs, with work site emerging as the strongest predictor. A significant majority of study participants (80.8 percent) were working in clinical areas not specifically designated for infectious disease care, indicating that the primary risk of infection resided within the general hospital environment rather than high-containment units [25]. This high prevalence in non-designated zones suggests a critical "protection gap" where a lower perceived risk likely led to relaxed IPC vigilance and suboptimal adherence compared to the high-threat environment of dedicated treatment wards [26] . Furthermore, the infection rate in low risk areas and among supporting staffs highlights a vulnerability to horizontal transmission in shared, non-clinical spaces where standard precautions are frequently deprioritized [26,27]. Mask Adherence Mask adherence was high (85.7–97.7%), aligning with facility-based findings in Eastern Ethiopia (88.3%) and Addis Ababa (85.7–93%) [28–30]. Conversely, our results exceed utilization rates reported in Northern Shewa (27.4%) and Northeastern Ethiopia (50.1%) (25) (26), a discrepancy that may reflect higher perceived risk and better resource availability in urban centers during the pandemic peak. Healthcare professionals demonstrated significantly higher mask adherence than supporting staff, a trend mirrored in previous research within police health facilities in Addis Ababa [33]. A clear spatial gradient in compliance emerged: HCWs in HPDA maintained significantly higher utilization rates than those in non-designated areas, a pattern documented in Uganda, Germany, China, and Ghana [34–37]. The presence of infection among these workers, notwithstanding high mask use, may suggest relaxation of protocols in staff-only areas facilitating peer-to-peer transmission, or potential social desirability bias where self-reported compliance is inflated compared to actual practice [38] [39]. Glove Adherence Glove adherence was high during AGPs (98.0%) but lower during surface contact (63.1%). While these rates exceed those reported in Uganda and Southwest Ethiopia [34] [40], they remain lower than the near-universal compliance observed in Burkina Faso, Saudi Arabia, Pakistan, and China [37,41–43] . A clear spatial gradient was evident, with significantly higher adherence among HCWs in HPDA compared to non-designated areas, mirroring findings from Uganda and Ghana [34] [41]. This disparity may be associated with risk perception; HCWs may maintain rigorous IPC measures when perceiving a direct, immediate threat to personal safety, such as during AGPs or within high-containment units [44]. Notably, the gap between healthcare professionals and supporting staff is wide in terms of glove utilization during interactions with body fluids and patient materials, indicating that supporting staff, who often handle infectious waste or contaminated linens, may represent the least protected group during high-risk fluid exposure. Such professional disparities may reflect inequities in safety training and the systemic exclusion of non-clinical staff from formal IPC orientation. Conversely, lower compliance during surface contact may reflect habituation to low-threat tasks or failure to recognize the environment as a viable transmission vehicle [45]. N95 Respirator Utilization and Fit Testing N95 use was high during AGPs (66.5%) but low during face-to-face contact (<40%), suggesting that HCWs prioritize N95 use for the most visible risks while reverting to medical masks for routine care. While our utilization rate exceeds reports from Nigeria (<10%) and aligns with previous findings in Addis Ababa (21.2%), it remains suboptimal for comprehensive protection [29] [46]. A critical finding was the near-total absence of N95 fit testing, with 98.9% of HCWs indicating their respirators were never tested, a rate significantly lower than those reported in Qatar and Australia [47] [48]. This suggests a profound institutional infrastructure gap in Ethiopia, where a "one-size-fits-all" procurement approach, combined with a lack of specialized testing kits, renders formal fit testing a systemic impossibility. Furthermore, 65% of respondents failed to perform a pre-use seal check, a failure rate notably higher than reported in Nepal [49]. The widespread absence of both professional fit testing and individual seal checks results in compromised safety margins, as a poorly fitted N95 respirator may offer no more functional defense than a standard surgical mask against submicron particles [50,51], and the superior filtration capacity of the respirator may be bypassed by peripheral leakage [52]. Determinants of Seal-Check Adherence Multivariable analysis identified work site, age, stress at the time of the event, communal living, IPC training, and involvement in AGPs as significant predictors of pre-use seal-check adherence, with work site emerging as the primary determinant. HCWs in HPDA demonstrated superior compliance, likely due to a heightened safety culture and peer supervision, whereas those in GHR areas appeared to operate under a "false sense of security." This suggest that environmental context may override procedural necessity, leading to a relaxation of WHO-recommended protocols in general wards despite identical biological risks [53]. Furthermore, HCWs aged more than 30 years had lower odds of adherence, potentially reflecting generational gaps in recent pandemic-related training or lower risk perception compared to younger cohorts or a greater awareness of evolving respiratory protection guidelines. Adherence was also significantly compromised by stress at the time of the event, which likely impairs the cognitive function and attention to detail required for safety protocols. This finding suggests that institutional interventions, including mental health support, are critical for indirectly bolstering IPC adherence [54]. Conversely, HCWs in communal or family living arrangements were more than twice as likely to perform seal checks, likely driven by "altruistic protection" and a heightened desire to safeguard household members. Finally, the positive associations with IPC training and AGP involvement highlight the efficacy of targeted institutional interventions. Involvement in high-risk procedures like AGPs likely reinforces the perceived severity of exposure, leading to better safety hygiene. Our results suggest that institutional safety culture must move beyond specialized training to address the environmental and psychological barriers, such as stress and perceived area-specific risk that hinder universal N95 seal check compliance. Donning and Doffing Adherence Errors in donning and doffing PPE lead to autoinoculation and pathogen transmission to HCWs [55] [56]. Our descriptive data revealed a knowledge‑behavior gap across professional roles, though professional category was not significant after multivariable adjustment. Supporting staff exhibited a knowledge gap (53.1% unaware of steps), whereas HCPs showed a behavioral gap (7.3% unaware but 56.8% never followed procedures), suggesting HCPs need implementation support and supporting staff require basic education. These descriptive differences align with studies from Nepal and Saudi Arabia linking profession and workplace to knowledge [57] [58]. A stark contrast in adherence emerged between designated infectious units and non‑designated wards, with proper technique declining significantly as perceived risk moved away from specialized units—rates lower than those reported in Saudi Arabia, India, and Canada [59–61]. Multivariable analysis confirmed work site as the strongest driver of donning/doffing adherence. Compared to HPDA, HCWs in non‑designated areas had markedly lower odds: GHR (AOR 0.12 [0.07–0.19]), MRCA (AOR 0.08 [0.04–0.14]), and LRS (AOR 0.07 [0.03–0.17]), representing 88–93% lower adherence. This steep gradient likely reflects the cognitive demands of doffing, which often occurs at shift end when staff are exhausted, despite being the period of highest self‑contamination risk [62]. Superior adherence in designated units aligns with evidence that specialized wards maintain more rigorous technical culture and supervision [36] . Involvement in AGP (AOR 3.61 [2.33–5.58]) and prior IPC training (AOR 1.59 [1.05–2.41]) were also significant predictors, supporting the premise that risk perception drives protective behaviors [63]. Eye Protection Systematic reviews have established that mandatory eye protection, particularly face shields, significantly reduces infection risk among HCWs [64]. Despite this, eye protection utilization was low (23–52.8%) across interaction types. Adherence was highest during AGPs and direct face-to-face contact but dropped during tasks involving body fluid exposure, surface decontamination, or handling of contaminated materials. This pattern suggests that HCWs may prioritize eye protection only when splash or inhalation threats are immediately visible, rather than maintaining it as a universal precaution. These low rates align with findings from the South Wollo Zone (31.9%) and Addis Ababa (19%), Ethiopia [29] [31]. While suboptimal adherence has been noted in Burkina Faso, Saudi Arabia, and Pakistan (1.56%, 68%, and 45%, respectively), our results stand in sharp contrast to the 100% compliance reported among Korean frontline nurses [41–43] [65]. This low utilization may stem from significant practical barriers documented in previous literature; HCWs frequently report that goggles or shields impair visibility due to fogging, while heat and dehydration issues reported by up to 76% of participants in similar settings hinder sustained use [66]. Furthermore, the tendency to omit eye protection during surface or material handling suggests a failure to recognize the environment as a viable transmission vehicle. This inconsistent approach, driven by perceived rather than actual risk, likely contributes to the vulnerability of staff working in non-specialized areas where standard precautions are frequently deprioritized [67]. Fluid-Resistant PPE Utilization of disposable gowns, coverall suits, head caps, and shoe covers remained below 50% across most patient interactions, although rates increased progressively from non-designated areas to HPDA. This low adherence mirrors a previous Addis Ababa study reporting hair protection and gown use at 18% and 72.4%, respectively [30], and aligns with findings from Southwest Ethiopia (39.8%) [40]. However, these results contrast sharply with significantly higher gown utilization reported in Korea (98%), Burkina Faso (95.6%), Pakistan (90%), and Saudi Arabia (85%) [41–43] [65]. This disparity might be attributed to systemic resource constraints in Ethiopia, where a lack of medical supplies persists as a primary barrier to universal precaution adherence, even among HCWs with strong conceptual understanding of PPE [68]. During body fluid contact, a massive precaution gap exists: while mask and glove use reached 97.7%, over 70% of staff omitted shoe covers and over 50% failed to use gowns or shields. Behavior was dictated by work site rather than objective biological risk; HCWs adhered strictly to protocols in HPDA but relaxed standards in MRCA or LRS zones, evidenced by extreme standardized residuals for face shields (SR = 11.7) and coveralls (SR = 13.0). This site-dependent safety culture persists despite high infection rates in non-specialized areas, proving that materials from GHR and MRCA zones remain highly infectious. Furthermore, support staff handling heavy contamination exhibited significantly lower adherence than clinicians (P < 0.001), being less likely to use gloves (58.5% vs. 74.6%, P = 0.013). This indicates that risk perception is frequently tethered to professional status and departmental labels rather than the objective presence of pathogens. Hand Hygiene practice Hand hygiene adherence was reactive, ranging from 49.3% before face-to-face contact to 82.5% after contact with body fluids. Studies from Nigeria and India reported superior adherence compared to our results and a similar study from Greece [69–71]. A multicenter study from Ethiopia documented an 81.4% compliance rate during the pandemic [72], while a recent meta-analysis revealed a lower 38% pooled compliance among Ethiopian HCWs, though a subgroup analysis showed a higher rate of 73% within Addis Ababa [31]. Global trends reflect a significant increase in hand hygiene since the pandemic began, with a recent review reporting a 74% overall compliance rate compared to pre pandemic levels which ranged from 5% to 89% [73]. In our study, adherence was highest (>80%) following contact with body fluids or contaminated surfaces. Our finding that adherence was highest (>80%) following contact with body fluids aligns with meta-analytical evidence showing that HCWs prioritize hand hygiene after body fluid exposure [73] ; our study reinforces this with a drop to 49.3% prior to contact compared to 74.4% after interactions. This disparity suggests that hand hygiene may be driven by a desire for self-protection rather than patient safety, as adherence increases when the perceived risk of acquiring infection is highest [74] [75]. Such reactive adherence patterns create a significant window for healthcare associated infections, as the most critical step for preventing cross contamination, hand hygiene prior to contact, remains the least practiced. HPDA consistently demonstrated better hand hygiene compliance than non-designated areas, a finding supported by research from university hospitals in Germany and Uganda [34,36]. However, our study identified a unique "Body Fluid Exception" where the site of work did not significantly influence adherence (p = 0.215). This suggests that disgust or the visible presence of fluids serves as a universal psychological trigger for hand hygiene that overrides the risk label of the ward. This innate behavioral driver represents a natural mechanism for IPC that could be leveraged in future training to cultivate more consistent hygiene habits across all clinical settings. We also demonstrated that healthcare professionals maintained consistently better compliance than support staff, which is consistent with meta analyses identifying clinicians and nurses as having greater adherence than nonclinical staff in Ghana, Uganda, and Somalia [35] [73,76,77]. The drop from 89.2 percent among professionals to a mere 16.7 percent among supporting staff in hand hygiene after body fluid exposure is s alarming. Given that supporting staff often handle infectious waste and contaminated linens, this 72.5 percent gap might represent a major institutional transmission pathway. Limitations of the study This study has several limitations. First, adherence data were self-reported, introducing potential recall and social desirability bias. Participants may have overestimated their adherence due to social desirability, or underestimated due to recall lapses over the 14-day window. Second, the cross-sectional design precludes causal inference regarding factors associated with adherence or infection; we can only report associations, not causal pathways. Third, the study period (March 2020 to March 2021) coincided with evolving IPC guidelines and variable PPE supply chains, which may affect generalizability to current contexts where guidelines have stabilized and supply chains have improved. Fourth, the focus on infected HCWs only may introduce selection bias, as adherence patterns among uninfected HCWs may differ. Fifth, the single-country setting limits generalizability to other healthcare systems with different resource levels, training infrastructures, and cultural contexts. Sixth, the absence of objective adherence measures (e.g., direct observation) means reported practices may not fully reflect actual behavior. Seventh, the study did not assess whether HCWs received formal fit testing or seal-check training before the pandemic, which may have influenced practices during the study period. Conclusions and Recommendations This study identifies significant protection gaps across multiple IPC domains, characterized by suboptimal N95 fit-testing, inconsistent pre-use seal checks, donning and doffing non-adherence, and reactive hand hygiene practice. These deficits form a distinct adherence gradient where better practices in designated settings prepared for high-consequence pathogens and among health professionals contrast with systemic vulnerabilities in general clinical environments and among support staff. With most infections occurring outside specialized units, primary occupational risk resides in general wards, driven by work site as an important determinant of adherence. Multivariable analysis confirms lower compliance odds in non-designated settings for both donning/doffing and pre-use seal-checks, with AGP involvement and IPC training acting as key positive predictors. To strengthen institutional resilience against high-consequence pathogens, we recommend standardization of IPC protocols across all clinical environments. Abbreviations The following abbreviations are used in this manuscript: AGP: Aerosol‑generating procedure; AIC: Akaike Information Criterion; AOR: Adjusted odds ratio; BIC: Bayesian Information Criterion; CDC: Centers for Disease Control and Prevention; CI: Confidence interval; COVID‑19: Coronavirus disease 2019; cOR: Crude odds ratio; EOC: Emergency Operating Center; ERC: Ethical Review Committee; GHR: General High‑Risk Areas; HCP: Healthcare professional; HCW: Healthcare worker; HPDA: High‑Pathogen Designated Areas; ICU: Intensive care unit; IPC: Infection prevention and control; IQR: Interquartile range; LRS: Low‑Risk Support Areas; MRCA: Medium‑Risk Clinical Areas; N95: N95 respirator; OSHA: Occupational Safety and Health Administration; PPE: Personal protective equipment; ROC‑AUC: Receiver operating characteristic – area under the curve; RT‑PCR: Reverse transcription polymerase chain reaction; SD: Standard deviation; SR: Standardized residual; SS: Supporting staff; VIF: Variance inflation factor; WHO: World Health Organization. Declarations Author Contributions: ZA: conceptualization, data curation, methodology, project administration, formal analysis, investigation, visualization, software, supervision, writing of the original draft, review and editing. TE: software, formal analysis, review and editing BR: conceptualization, supervision, project administration, Funding acquisition WW: conceptualization, supervision, project administration, review and editing SG: conceptualization, Writing – original draft, review and editing AH and BE: data curation, writing original draft, review and editing MW,RWY and SY: conceptualization, supervision, resources BB: Software and formal analysis TF and ZC: conceptualization, project administration, funding acquisition, resources, supervision BH: conceptualization, data curation, formal analysis, methodology, writing original draft, review and editing. Funding: The authors received funding from the Ministry of Health, Ethiopia. Institutional Review Board Statement: This research was reviewed and approved by the Ethical Review Committee (ERC) of the Addis Ababa Health Bureau (5204/227) in Ethiopia. Participants have given consent for their data to be used in the research. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study Data Availability Statement: Supporting data for the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that there are no competing interests. Consent for publication Not applicable. Conflicts of Interest The authors declare no conflicts of interest. References Maliszewska M, Mattoo A, Van der Mensbrugghe D. The Potential Impact of COVID-19 on GDP and Trade: A Preliminary Assessment. 2020. https://doi.org/10.1596/1813-9450-9211 COVID-19 cases | WHO COVID-19 dashboard [Internet]. datadot. [cited 2024 Nov 13]. https://data.who.int/dashboards/covid19/cases. 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Amdemariam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYJCCAwwMCQwMzAyMD4AcHj5StDAbgLSwEWlRAohgkwCThNTKt/cePPijIk3evJ35WeXXHDsZNgbmh49u4NFicOZcwgGJMzmGcw6zmd2W3ZYMdBibsXEOPi0SOQYHDNsqGGcwM5jdltzGDNTCwyaNT4v8/DcGBxL/VdjPYGb/Viy5rZ6wFoYbPAYHDjbkJM5g5jFj/LjtMGEtBmdyDA42HEtLBmoplmbcdpyHjZmAX+Tbzxh//FGTbDuD//jGjz+3Vdvzszc/fIzXYciAmQdMEqscBBh/kKJ6FIyCUTAKRgwAAJpCRLrtbHE+AAAAAElFTkSuQmCC","orcid":"","institution":"Zewditu Memorial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zekarias","middleName":"","lastName":"Amdemariam","suffix":""},{"id":629528054,"identity":"988b9fd8-73a9-457a-a94c-348e779a501a","order_by":1,"name":"Tewodros Eshete","email":"","orcid":"","institution":"Saint Paul Hospital Millennium Medical College","correspondingAuthor":false,"prefix":"","firstName":"Tewodros","middleName":"","lastName":"Eshete","suffix":""},{"id":629528055,"identity":"11675de7-8ded-40ca-b7e0-747da7836524","order_by":2,"name":"Ruth Woldeyohannes Yirgu","email":"","orcid":"","institution":"Zewditu Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruth","middleName":"Woldeyohannes","lastName":"Yirgu","suffix":""},{"id":629528056,"identity":"a2293249-d571-4660-a79b-e297f445c1c7","order_by":3,"name":"Birhane Redae","email":"","orcid":"","institution":"Federal Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Birhane","middleName":"","lastName":"Redae","suffix":""},{"id":629528057,"identity":"3585238c-e870-4836-8334-b689fc57f017","order_by":4,"name":"Woldesenbet Wagnew","email":"","orcid":"","institution":"Saint Paul Hospital Millennium Medical College","correspondingAuthor":false,"prefix":"","firstName":"Woldesenbet","middleName":"","lastName":"Wagnew","suffix":""},{"id":629528058,"identity":"afc0f6d4-6af9-4abd-8577-dc3da964d94a","order_by":5,"name":"Shewalem Geremew","email":"","orcid":"","institution":"Zewditu Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shewalem","middleName":"","lastName":"Geremew","suffix":""},{"id":629528059,"identity":"c2f81ce3-0515-4ed2-ba54-5b83e3928d64","order_by":6,"name":"Amanuel Hintsa","email":"","orcid":"","institution":"Zewditu Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Amanuel","middleName":"","lastName":"Hintsa","suffix":""},{"id":629528060,"identity":"89531567-397b-4714-aad0-937601c13e7c","order_by":7,"name":"Bezawit Endeshaw","email":"","orcid":"","institution":"Zewditu Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bezawit","middleName":"","lastName":"Endeshaw","suffix":""},{"id":629528061,"identity":"00459552-9c6a-4e60-9bc0-0e094ddf061a","order_by":8,"name":"Miraf Walelegn","email":"","orcid":"","institution":"Federal Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Miraf","middleName":"","lastName":"Walelegn","suffix":""},{"id":629528062,"identity":"fce581fd-0412-4a98-97bf-32a966a5dcc9","order_by":9,"name":"Sisay Yifru","email":"","orcid":"","institution":"Federal Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Sisay","middleName":"","lastName":"Yifru","suffix":""},{"id":629528063,"identity":"773b99e6-f07f-494f-a271-3ab6509c7117","order_by":10,"name":"Biniyam Belayneh","email":"","orcid":"","institution":"ABH Partners PLC","correspondingAuthor":false,"prefix":"","firstName":"Biniyam","middleName":"","lastName":"Belayneh","suffix":""},{"id":629528064,"identity":"418f164c-15bd-4545-b091-af48fd88146e","order_by":11,"name":"Tsion Firew","email":"","orcid":"","institution":"Federal Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Tsion","middleName":"","lastName":"Firew","suffix":""},{"id":629528065,"identity":"420f5032-c79a-48d7-b549-3a4a1d512d63","order_by":12,"name":"Zelalem Chimdesa","email":"","orcid":"","institution":"Zewditu Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zelalem","middleName":"","lastName":"Chimdesa","suffix":""},{"id":629528066,"identity":"edea50dc-0d09-4e70-99fc-80b4ffca7c8a","order_by":13,"name":"Bisrat Hussein","email":"","orcid":"","institution":"Saint Paul Hospital Millennium Medical College","correspondingAuthor":false,"prefix":"","firstName":"Bisrat","middleName":"","lastName":"Hussein","suffix":""}],"badges":[],"createdAt":"2026-04-02 04:23:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9297921/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9297921/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107896221,"identity":"e86c48d3-89bc-4b07-92ec-a2ee3fa8086a","added_by":"auto","created_at":"2026-04-27 10:52:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147448,"visible":true,"origin":"","legend":"\u003cp\u003eA total of 1,430 HCWs with laboratory-confirmed COVID-19 were identified. After exclusions (n = 278), 1,152 HCWs completed structured interviews. Among these, 742 (64.4%) reported direct face-to-face contact with suspected or confirmed COVID-19 patients and were included in the donning and doffing adherence analysis. Of these, 350 (47.2%) reported using N95 respirators during face-to-face interactions and were included in the N95 pre-use seal-check and fit testing analysis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9297921/v1/3fe60797c6150e62e64b5f5b.png"},{"id":108180864,"identity":"a6f451f5-6a2b-4ca9-90c5-8cdf166ca2b7","added_by":"auto","created_at":"2026-04-30 08:54:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72427,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical exposure patterns among infected healthcare personnel in Addis Ababa, Ethiopia, March 2020–March 2021 (N=1,152).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLegend: \u003c/strong\u003eStacked bar chart showing the frequency of self-reported interactions in the 14 days preceding COVID-19 diagnosis. Data labels represent percentages (%); Navy = Yes, Cranberry = No, Gray = Don’t know. Among HCWs who contracted COVID-19, 64.4% (N = 742) reported direct face-to-face contact, 55.0% (N = 631) handled patient materials, 42.6% (N = 491) had contact with contaminated surfaces, 26.1% (N = 301) experienced exposure to body fluids, and 17.1% (N = 197) were present during aerosol-generating procedures (AGPs). Uncertainty about exposure was highest for contact with surrounding surfaces (9.4%), followed by handling patient materials (6.9%) and direct contact (6.9%); uncertainty was lowest for AGPs (0.4%). AGPs: aerosol-generating procedures.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9297921/v1/574ef09fedf0631ecafd4fb0.png"},{"id":107896224,"identity":"a0b204c8-e4af-462b-9fdb-0b9b44571469","added_by":"auto","created_at":"2026-04-27 10:52:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":152419,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictors of N95 respirator pre-use seal-check adherence among infected healthcare personnel in Addis Ababa, Ethiopia, March 2020–March 2021 (N = 350).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLegend:\u003c/strong\u003e This forest plot illustrates adjusted odds ratios (AOR) and 95% confidence intervals (CI), where values greater than 1.0 indicate an increased likelihood of seal-check performance. Communal or shared living includes healthcare workers residing with family, friends, or in communal camps. Clinical settings are compared against High-Pathogen Designated Areas (HPDA) as the reference group, with General High-Risk Areas (GHR) , Medium-Risk Clinical Areas (MRCA) , and Low-Risk Support Areas (LRS) representing descending levels of clinical risk. The model accounts for institutional safety and training factors, including prior infection prevention and control (IPC) training and involvement in aerosol-generating procedures (AGPs). Categorical variables such as age (\u0026gt;30 years) and stressful events are modeled against their respective reference groups to reflect the change in odds of adherence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel diagnostics:\u003c/strong\u003e Hosmer-Lemeshow χ² = 6.59 (P = 0.582), ROC-AUC = 0.714, mean VIF = 1.86\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9297921/v1/e2b2c232e1256e502a1409ae.png"},{"id":108184485,"identity":"8b8c0a88-cc39-4838-8a5b-91598d5c5f0f","added_by":"auto","created_at":"2026-04-30 09:04:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1740917,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9297921/v1/dd480260-77b8-412d-a863-4a8fb0c9bb61.pdf"},{"id":107896222,"identity":"a94bcf73-2a9b-400b-a3de-25a784ce2af6","added_by":"auto","created_at":"2026-04-27 10:52:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1182736,"visible":true,"origin":"","legend":"","description":"","filename":"questionniareIPCcovid.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9297921/v1/bd067131ad60dd1ef89b9c4b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Protection Gap: Infection prevention and control Adherence and Determinants among SARS-CoV-2 Infected Healthcare Workers in Ethiopia and Implications for Future High-Consequence Pathogen Outbreaks","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe emergence of high-consequence respiratory pathogens, most notably SARS-CoV-2, has imposed an unprecedented burden on global health, economic, and social systems. As of late 2024, the World Health Organization reported over 776\u0026nbsp;million confirmed cases and 7\u0026nbsp;million deaths globally, with Ethiopia recording over 500,000 cases and 7,500 deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The clinical spectrum of such infections varies widely, ranging from asymptomatic carriage to critical illness requiring intensive care [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Healthcare workers (HCWs) remain at the forefront of these outbreaks, facing disproportionate infection risks that are further exacerbated in resource-limited settings characterized by understaffing and constrained supplies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis vulnerability is particularly concerning in developing nations such as Ethiopia, where healthcare systems face chronic understaffing and resource constraints. The potential for infected HCWs to transmit infection in both hospital and community settings underscores the critical importance of robust infection prevention and control (IPC) measures[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This was recently underscored by the country's first ever outbreak of Marburg Virus Disease, which occurred between November 2025 and January 2026. Centered in the South Ethiopia and Sidama regions, the outbreak resulted in 14 confirmed cases and a high case fatality rate, with the Ministry of Health identifying infected healthcare workers among the casualties [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEffective IPC adherence is the primary defense against occupational acquisition of infectious diseases. This is achieved through systematic training, the provision and appropriate use of personal protective equipment (PPE), and continuous monitoring and evaluation of safety protocols [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, evidence suggests that IPC adherence is often inconsistent across different clinical environments [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While specialized isolation units often maintain high standards, general clinical areas may experience a \"risk perception gap\" that compromises safety [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. To address this, there is a need for a standardized, task-based evaluation of safety breaches across the entire healthcare facility [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eTherefore, this research aims to analyze IPC practices of HCWs with laboratory-confirmed infections who had direct face‑to‑face contact, were involved in aerosol‑generating procedures (AGPs), or experienced exposure to body fluids, materials, or surfaces of suspected or confirmed COVID‑19 patients, focusing on the critical 14-day window preceding their diagnosis. Utilizing a framework adapted from the OSHA Occupational Risk Pyramid, we categorized clinical environments into High-Pathogen Designated Areas (HPDA), General High-Risk Areas (GHR), Medium-Risk Clinical Areas (MRCA), and Low-Risk Support Areas (LRS). Furthermore, the study specifically assessed adherence to donning and doffing procedures and the performance of pre-use seal checks among HCWs to identify technical and behavioral gaps in respiratory protection. By identifying exactly where adherence fails, whether in specialized units or general wards, this study provides critical data for strengthening occupational safety and institutional resilience. These findings may inform scalable strategies for reinforcing protective measures against current and future health crises with similar transmission patterns.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis study employed a facility-based, comparative cross-sectional design to evaluate adherence to IPC protocols among HCWs in Addis Ababa, Ethiopia (March 2020\u0026ndash;March 2021). By analyzing safety breaches among HCWs with laboratory-confirmed RT-PCR diagnoses, the study identifies vulnerabilities in routine occupational safety relevant to future emerging infectious threats.\u003c/p\u003e \u003cp\u003eStudy Population\u003c/p\u003e \u003cp\u003eWe identified 1,430 HCWs with confirmed SARS-CoV-2 infection through the Emergency Operating Center database. Of these, 1,152 HCWs consented to participate in structured interviews regarding their practices 14 days prior to diagnosis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria:\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and Exclusion Criteria:\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInclusion criteria\u003c/b\u003e: HCWs aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years with laboratory-confirmed SARS-CoV-2 infection who were actively working in a healthcare facility and actively engaged in the care of suspected or confirmed COVID-19 patients during the 14 days prior to diagnosis, and who provided informed consent.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eExclusion criteria\u003c/b\u003e: HCWs who were unable to recall the 14-day period prior to diagnosis, those on extended leave during the exposure window, those who declined participation, those not actively working in a healthcare facility, or those not actively engaged in the care of suspected or confirmed COVID-19 patients during the 14 days prior to diagnosis.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipant Flow Diagram\u003c/h3\u003e\n\u003cp\u003eThis study defined two distinct analytical populations for multivariable analysis. First, donning and doffing adherence was assessed among the 742 HCWs who reported direct face-to-face contact with suspected or confirmed COVID-19 patients (representing 64.4% of all infected HCWs). Second, N95 pre-use seal-check adherence and fit testing were assessed among the subset of 350 HCWs from this group who reported using N95 respirators during face-to-face interactions (representing 47.2% of the face-to-face group and 30.4% of all infected HCWs). These subpopulations are clearly indicated in the respective multivariable models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHCW\u0026thinsp;=\u0026thinsp;healthcare worker; COVID-19\u0026thinsp;=\u0026thinsp;coronavirus disease 2019; AGPs\u0026thinsp;=\u0026thinsp;aerosol-generating procedures; EOC\u0026thinsp;=\u0026thinsp;Emergency Operating Center\u003c/p\u003e \u003cp\u003eData Collection Tools, Procedures and Quality control measures\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe study used standardized and structured questionnaires for the purpose of data collection. The questionnaire was developed after relevant literature, the WHO risk assessment tool, and Internet sources were reviewed [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The questionnaire was pilot-tested with 30 HCWs not included in the final sample to assess clarity and comprehensibility. The data were collected via a paper-based questionnaire, which was filled by trained health care professionals by interviewing each study participant. The questionnaire assessed participants' compliance with standard IPC practices, PPE utilization, and hand hygiene protocols during the 14 days preceding their COVID-19 diagnosis. Supervisors conducted daily evaluations of completed questionnaires to ensure completeness, accuracy, and adherence to standardized procedures.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOperational Definitions\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHealthcare Worker Categories\u003c/strong\u003e \u003cp\u003eIn this study, we classified HCWs into supporting staff and health care professionals. The supporting staff comprises of admission and reception clerks, patient transporters, catering personnel, cleaners, ambulance drivers, security guards, administrative workers, and morgue professionals. In contrast, health care professionals include medical doctors, nurses, health officers, anesthetists, laboratory personnel, dentists, midwives, and pharmacists.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRisk classification based on COVID-19 exposure\u003c/strong\u003e \u003cp\u003eWe adapted the OSHA Occupational Risk Pyramid for Infectious Diseases [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] to categorize clinical environments based on the potential for contact with infectious sources. HCWs were classified into four distinct areas based on their exposure risk.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHigh-Pathogen Designated Areas (HPDA)\u003c/b\u003e: Healthcare settings specifically involved in the isolation and treatment of confirmed or suspected infectious cases. This includes designated treatment centers, isolation areas, fever clinics, and medical transport operators moving known cases.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGeneral High-Risk Areas (GHR)\u003c/b\u003e: Areas with high potential for exposure during specialized medical, postmortem, or laboratory procedures, particularly environments where aerosol-generating procedures (AGPs) are frequently performed, such as intensive care units, emergency rooms, and operating theaters.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMedium-Risk Clinical Areas (MRCA)\u003c/b\u003e: Settings involving frequent or close contact with the general patient population who are not confirmed cases. This includes roles in general inpatient and outpatient departments, labor wards, laboratories, and pharmacies.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLow-Risk Support Areas (LRS)\u003c/b\u003e: Roles involving minimal patient interaction or primarily administrative functions, such as administrative offices, laundry facilities, and support staff operating in non-clinical zones.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAdherence Assessment\u003c/strong\u003e \u003cp\u003eFor each PPE item, hand hygiene action, and donning/doffing procedure, adherence was defined as correct practice in \u0026gt;\u0026thinsp;50% of relevant interactions during the 14‑day period prior to diagnosis. This threshold was chosen to pragmatically distinguish HCWs who performed a given IPC measure more often than not, recognizing that 100% adherence is rarely achievable in resource‑limited settings.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSpecific IPC Measures\u003c/strong\u003e \u003cp\u003eAdherence was defined as utilization of a specified PPE item for more than 50% of relevant interactions during the 14-day period prior to diagnosis. The study evaluated whether N95 respirators were test-fitted and whether pre-use seal checks were performed. Fit testing confirms that the N95 respirator forms a tight seal on the user's face before use, as some respirators fail stipulated safety thresholds [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. OSHA enforcement guidance recommends initial fit tests for each HCW with the same model, style, and size respirator before use [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. CDC guidelines recommend seal checks before each use following annual fit testing [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePre-use seal check\u003c/strong\u003e \u003cp\u003eA pre-use seal check is a user-performed verification that an N95 respirator forms an adequate facial seal before each entry into a patient care area. A \u003cb\u003eseal check\u003c/b\u003e is performed each time a respirator is used to ensure a facial seal that minimizes particle bypass through gaps between the wearer's skin and the N95 seal [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This study assessed pre-use seal check practices only among participants who reported using N95 respirators during face-to-face interactions with suspected or confirmed COVID-19 patients (N\u0026thinsp;=\u0026thinsp;350). Participants were asked whether they performed a seal check before entering the clinical environment.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDonning\u003c/strong\u003e \u003cp\u003eDonning is defined as appropriate application and use of PPE to achieve intended protection by reducing exposure risk; doffing is the systematic removal of PPE to avoid self-contamination [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003eData analysis procedure\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eData were entered using Epi Info and analyzed with Stata version 14.0 (StataCorp, College Station, TX, USA).\u003c/p\u003e\u003cp\u003eDescriptive Analysis: Continuous variables were assessed for normality using the Shapiro-Wilk test and visual inspection of histograms. Normally distributed continuous variables are presented as means with standard deviations (SDs); non-normally distributed variables are presented as medians with interquartile ranges (IQRs). Categorical variables are summarized as frequencies and percentages.\u003c/p\u003e\u003cp\u003eBetween-group comparisons for categorical variables were conducted using Pearson's chi-square test, with Fisher's exact test applied when expected cell frequencies were less than five. To quantify the magnitude of associations, Cram\u0026eacute;r's V effect size measures were calculated, with values interpreted as: \u0026gt;0.10\u0026thinsp;=\u0026thinsp;small effect, \u0026gt;\u0026thinsp;0.30\u0026thinsp;=\u0026thinsp;medium effect, \u0026gt;\u0026thinsp;0.50\u0026thinsp;=\u0026thinsp;large effect. For significant chi-square results, standardized residuals were examined, with values exceeding\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 considered statistically significant at α\u0026thinsp;=\u0026thinsp;0.05, enabling identification of specific group differences contributing to overall associations.\u003c/p\u003e\u003cp\u003eTwo separate multivariable logistic regression models were constructed to identify independent predictors of: (1) pre-use N95 seal-check adherence among N95 users (N\u0026thinsp;=\u0026thinsp;350), and (2) appropriate donning and doffing practices (N\u0026thinsp;=\u0026thinsp;742). Variables with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.2 in univariable logistic regression were considered for entry into the multivariable models. For the \u003cb\u003eN95 seal-check\u003c/b\u003e model (outcome: performing pre-use seal check, yes/no), the initial univariable analysis included comorbidity, stress level at the time of event, history of formal N95 fit testing, living status, involvement in AGPs, prior IPC training, clinical setting, age category, and years of professional service. Variables meeting the P\u0026thinsp;\u0026lt;\u0026thinsp;0.2 threshold were entered into the multivariable model. Backward elimination was then applied to minimize the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), yielding the final parsimonious model. For the \u003cb\u003edonning and doffing\u003c/b\u003e model (outcome: following recommended procedures for \u0026gt;\u0026thinsp;50% of interactions, yes/no), the initial univariable analysis included profession, clinical setting, age category, gender, prior IPC training, years of professional service, marital status, stress level at the time of event, history of substance abuse, and average working hours per day. Variables meeting the P\u0026thinsp;\u0026lt;\u0026thinsp;0.2 threshold were entered into the multivariable model, followed by backward elimination to minimize AIC and BIC.\u003c/p\u003e\u003cp\u003eModel diagnostics, including the Hosmer-Lemeshow goodness-of-fit test, area under the receiver operating characteristic curve (ROC-AUC), and variance inflation factor (VIF), confirmed appropriate model fit, good discriminatory ability, and no significant multicollinearity for both final models. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed). Results from multivariable models are presented as adjusted odds ratios (AOR) with 95% confidence intervals (CI).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e This study was approved by the Ethical Review Committee of the Addis Ababa Health Bureau (5204/227). Permission was obtained from the Addis Ababa Health Bureau, the healthcare facilities where infected HCWs were employed, and the Addis Ababa Emergency Operating Center. Written informed consent was obtained from all participants. Throughout data collection, confidentiality was protected. Participation was voluntary, and participants could withdraw at any time, even after the interview had begun, and could decline to answer any question. All methods were implemented in accordance with relevant guidelines and regulations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eA total of 1,152 HCWs with laboratory-confirmed COVID-19 were included. The median age of participants was 29 years (IQR: 27\u0026ndash;34; range: 19\u0026ndash;88). The median duration of professional experience was 5 years (IQR: 3\u0026ndash;8), and the median monthly income was 8,017 Ethiopian Birr. A significant majority (80.8%) of participants worked in areas not specifically designated for COVID-19 care prior to diagnosis. Based on the adapted OSHA Occupational Risk Pyramid, the distribution of work locations in the 14 days prior to diagnosis was: GHR \u0026ndash; 349 (30.3%), MRCA \u0026ndash; 400 (34.7%), LRS \u0026ndash; 182 (15.8%), and HPDA \u0026ndash; 221 (19.2%). Among infected participants, 1,048 (91.0%) were healthcare professionals, while the remainder were supporting staff. Among healthcare professionals, 412 (35.8%) were doctors and 434 (37.7%) were nurses or midwives. Notable disparities in IPC training coverage were observed by work location, ranging from 31.5% in MRCA to 57.5% in HPDA. Self-reported stress at the time of diagnosis was highest among HCWs in HPDA (62.4%) and GHR (57.6%) (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic and occupational characteristics of healthcare personnel infected during the study period in Addis Ababa, Ethiopia from March 2020 to March 2021\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eSocio demographic characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c3\" namest=\"c2\" rowspan=\"3\"\u003e\n \u003cp\u003eTotal N(%)/Median with IQR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"8\" nameend=\"c11\" namest=\"c4\"\u003e\n \u003cp\u003ePlace of work before diagnosis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Designated Areas\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;931) (80.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\n \u003cp\u003e\u003cstrong\u003eDesignated Areas (N\u0026thinsp;=\u0026thinsp;221) (19.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRS (N\u0026thinsp;=\u0026thinsp;182) (15.80%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRCA\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;400) (34.72%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\n \u003cp\u003e\u003cstrong\u003eGHR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;349) (30.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\n \u003cp\u003e\u003cstrong\u003eHPDA (N\u0026thinsp;=\u0026thinsp;221) (19.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge in years\u003c/p\u003e\n \u003cp\u003eMedian [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e29[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e32.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYear of professional\u003c/p\u003e\n \u003cp\u003eexperience Median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e4[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMonthly income (ETB), Median [IQR] (n\u0026thinsp;=\u0026thinsp;1,118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e8017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e[6000,\u003c/p\u003e\n \u003cp\u003e10470]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e6500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e[3338,\u003c/p\u003e\n \u003cp\u003e8170]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e[6193,\u003c/p\u003e\n \u003cp\u003e10470]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e9000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e[6193,\u003c/p\u003e\n \u003cp\u003e11300]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e8300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e[6000,\u003c/p\u003e\n \u003cp\u003e10150]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e45.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e41.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e37.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e50.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e55.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e54.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e58.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e63.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e49.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e44.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e45.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e54.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e48.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e46.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e33.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e51.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e38.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e49.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e52.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e63.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWidowed/divorced/\u003c/p\u003e\n \u003cp\u003eseparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e3.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\n \u003cp\u003eProfession of the health care workers (N\u0026thinsp;=\u0026thinsp;1152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDoctors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e35.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e39.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e44.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e39.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNurses/midwife\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e37.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e7.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e47.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e39.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e42.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHealth officers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e6.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e4.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e3.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSupporting staffs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e29.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e8.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLab. professionals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e32.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e2.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePharmacist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e17.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAnesthetist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e6.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOther healthcare professionals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e0.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\n \u003cp\u003eIPC training(N\u0026thinsp;=\u0026thinsp;1152)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e61.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e68.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e67.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e42.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e38.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e32.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e57.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLiving status (N\u0026thinsp;=\u0026thinsp;1152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAlone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e21.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWith a family, friends or in a camp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e78.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e85.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e74.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e79.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\n \u003cp\u003eStressed at the time of the event\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e44.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e51.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e46.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e42.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e55.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e48.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e53.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e57.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eETB\u0026thinsp;=\u0026thinsp;Ethiopian Birr; HPDA\u0026thinsp;=\u0026thinsp;High-Pathogen Designated Areas; GHR\u0026thinsp;=\u0026thinsp;General High-Risk Areas; MRCA\u0026thinsp;=\u0026thinsp;Medium-Risk Clinical Areas; LRS\u0026thinsp;=\u0026thinsp;Low-Risk Support Areas; IQR\u0026thinsp;=\u0026thinsp;interquartile range; IPC\u0026thinsp;=\u0026thinsp;infection prevention and control.\u003c/p\u003e\n\u003ch3\u003eHCW interactions with patients in the 14 days prior to diagnosis\u003c/h3\u003e\n\u003cp\u003eAmong HCWs who contracted COVID-19, 64.4% (n\u0026thinsp;=\u0026thinsp;742) reported direct face-to-face contact with suspected or confirmed cases, 55.0% (n\u0026thinsp;=\u0026thinsp;631) handled patient materials, 42.6% (n\u0026thinsp;=\u0026thinsp;491) had contact with contaminated surfaces, 26.1% (n\u0026thinsp;=\u0026thinsp;301) experienced exposure to body fluids, and 17.1% (n\u0026thinsp;=\u0026thinsp;197) were present during AGPs. Uncertainty about exposure was highest for contact with surrounding surfaces (9.4%), followed by handling patient materials (6.9%) and direct contact (6.9%); uncertainty was lowest for AGPs (0.4%) (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePPE Use during Direct Face-to-Face Interactions\u003c/h3\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eDuring direct face-to-face interactions, 87.5% of participants used medical masks for more than half of interactions, and 68.2% did so for gloves. Utilization declined significantly from HPDA to LRS for both mask and glove use (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, V\u0026thinsp;=\u0026thinsp;0.15 and V\u0026thinsp;=\u0026thinsp;0.23, respectively). Regarding specialized respiratory protection, only 31.3% of participants used N95 respirators for more than half of interactions, with strong variation by work site (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, V\u0026thinsp;=\u0026thinsp;0.36). In HPDA settings, 74.8% used N95 respirators for more than half of interactions, significantly higher than expected (standardized residual\u0026thinsp;=\u0026thinsp;9.8), compared to 13.0\u0026ndash;26.6% in non-designated areas. Among participants who used N95 respirators during face-to-face interactions (N\u0026thinsp;=\u0026thinsp;350), pre-use seal-check compliance was low overall (34.9%), ranging from 20.7% in MRCA to 48.1% in HPDA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, V\u0026thinsp;=\u0026thinsp;0.24). Formal N95 fit testing was nearly absent, with only 1.1% of these participants reporting test-fitted respirators and none in MRCA or LRS (P\u0026thinsp;=\u0026thinsp;0.328).\u003c/p\u003e\n \u003cp\u003eUtilization of face shields, disposable gowns, coverall suits, head caps, and shoe covers exceeded 50% of the time in less than 25% of participants overall. Work site was significantly associated with utilization of all these PPE items (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all), with moderate to large effect sizes: coverall suits (V\u0026thinsp;=\u0026thinsp;0.43), face shields (V\u0026thinsp;=\u0026thinsp;0.42), disposable gowns (V\u0026thinsp;=\u0026thinsp;0.34), head caps (V\u0026thinsp;=\u0026thinsp;0.30), and shoe covers (V\u0026thinsp;=\u0026thinsp;0.34). In addition, disparities in PPE adherence were also evident by professional role. Supporting staff reported less frequent utilization of medical masks (P\u0026thinsp;=\u0026thinsp;0.011, V\u0026thinsp;=\u0026thinsp;0.11) and single-use gloves (P\u0026thinsp;=\u0026thinsp;0.011, V\u0026thinsp;=\u0026thinsp;0.16) compared to healthcare professionals. No significant differences by professional role were observed for other PPE items or for N95 seal-check and fit testing (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdherence to personal protective equipment utilization, donning and doffing procedures during direct patient interactions stratified by clinical setting and professional role in Addis Ababa, Ethiopia from March 2020 to March 2021\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eExposure Modality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eType of\u003c/p\u003e\n \u003cp\u003ePPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003ePPE\u003c/p\u003e\n \u003cp\u003euse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\n \u003cp\u003ePlace of work before the diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\n \u003cp\u003eOccupation of the HCWs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003eHPDA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003eGHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCPs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e% (SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e% (SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e%(SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e%(SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e%(SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e%(SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"37\" rowspan=\"38\"\u003e\n \u003cp\u003ePPE\u003c/p\u003e\n \u003cp\u003euse\u003c/p\u003e\n \u003cp\u003eduring direct\u003c/p\u003e\n \u003cp\u003eface to\u003c/p\u003e\n \u003cp\u003eface\u003c/p\u003e\n \u003cp\u003econtact\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;742)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eMedical mask\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e49(6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4.4 (-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2.3(-2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e9.8(1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e14.8(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e6.3(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e10.6(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e44(5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.5(-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e6.6(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4.9(-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e13.6(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e5.3(-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e14.9(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e649(87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e93.1(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e91.0(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e85.4(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e71.6(-1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e88.3(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e74.5(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.011 (V\u0026thinsp;=\u0026thinsp;0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eSingle use gloves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e161(21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e10.7(-3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e15.2(-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e26.0(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e50.6(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e20.0(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e46.8(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e75(10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e5.0(-2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e9.4(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e15.0(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e7.4(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e10.5(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e4.3(-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e506(68.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e84.3(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e75.4(1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e58.9(-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e42.0(-2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e69.5(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e48.9(-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.011 (V\u0026thinsp;=\u0026thinsp;0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eN-95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e392(52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16.4(-6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e56.3(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e66.7(3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e71.6(2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e51.8(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e68.1(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e118(15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e8.8(-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e17.2(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e20.3(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e12.3(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e16.1(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e12.8(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e232(31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e74.8(9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e26.6(-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e13.0(-5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e16.0(-2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e32.1(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e19.1(-1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.087 (V\u0026thinsp;=\u0026thinsp;0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eSeal check of N-95 (N-350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e228(65.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e51.9(-1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e67.9(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e79.3(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e78.3(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e65.4(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e60.0(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e122(34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e48.1 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e32.1(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e20.7(-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e21.7(-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e34.6(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e40.0(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2(Cramer\u0026apos;s V)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.669 (V\u0026thinsp;=\u0026thinsp;0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eFit test of N-95\u003c/p\u003e\n \u003cp\u003e(N-350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e346(98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e97.7(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e99.1(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e100.0(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e100.0(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e98.8(-0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e100.0(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.3(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.9(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.0(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1.2(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e0.0(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.328 (V\u0026thinsp;=\u0026thinsp;0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;1.000 (V= -0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eFace shields/ googles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e475(64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e22.0(-6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e70.7(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e80.1(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e76.5(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e63.3(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e74.5(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e96(12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e8.2(-1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e16.4(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e13.4(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e9.9(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e13.1(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e10.6(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e171(23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e69.8(12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e12.9(-3.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e6.5(-5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e13.6(-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e23.6(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e14.9(-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2(Cramer\u0026apos;s V)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.285 (V = 0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eDisposable gowns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e509(68.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e34.0(-5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e73.0(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e82.5(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e80.2(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e68.2(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e74.5(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e75(10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e8.2(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e12.9(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e8.9(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e8.6(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e10.1(-0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e10.6(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e158(21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e57.9(10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e14.1(-2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e8.5(-4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e11.1(-2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e21.7(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e14.9(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.585 (V\u0026thinsp;=\u0026thinsp;0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eCoverall\u003c/p\u003e\n \u003cp\u003esuit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e565(76.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e34.0(-6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e83.2(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e93.5(3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e84.0(0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e75.8(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e80.9(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e53(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6.9(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e9.0(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e5.3(-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e7.4(0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e6.9(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e10.6(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e124(16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e59.1(13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.8(-3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.2(-5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e8.6(-1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e17.3(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e8.5(-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.189 (V\u0026thinsp;=\u0026thinsp;0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eHead cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e470(63.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e30.2(-5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e63.3(-0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e79.3(3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e80.2(1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e62.4(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e76.6(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e61(8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e7.5(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e9.8(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e7.3(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e7.4(-0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e8.5(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e4.3(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e211(28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e62.3(8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e27.0(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e13.4(-4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e12.3(-2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e29.1(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e19.1(-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.170 (V\u0026thinsp;=\u0026thinsp;0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eShoe covers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e603(81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e48.4(-4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e86.7(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e93.5(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e91.4(1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e80.9(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e87.2(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e61(8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e15.1(3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.2(-0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4.9(-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e4.9(-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e8.5(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e4.3(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e78(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e36.5(10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.1(-2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.6(-4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e3.7(-1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e10.6(0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e8.5(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.624 (V\u0026thinsp;=\u0026thinsp;0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\n \u003cp\u003eDonning /doffing of PPE during direct face to face contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e423(57.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e22.0(-5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e60.9(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e75.6(3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e56.8(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e58.9(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e29.8(-2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e72(9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e10.0(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e15.2(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e5.7(-2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e3.7(-1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e10.2(0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e2.1(-1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e175(23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e64.2(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e17.6(-2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e8.1(-5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e9.9(-2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e24.2(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e14.9(-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003eDon\u0026rsquo;t know the steps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e72(9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3.8(-2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e6.3(-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e10.6(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e29.6(5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e6.8(-2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e53.2(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSR= standardized residuals; High-Pathogen Designated Areas (HPDA); General High-Risk Areas (GHR) ; Medium-Risk Clinical Areas (MRCA) ;Low-Risk Support Areas (LRS); HCPs=health care professionals; SS= supporting staff. Statistical significance (P-values) and effect sizes (Cramer\u0026apos;s V) are included to illustrate the strength of the association between workplace designation and protocol compliance.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eMultivariable Analysis: Predictors of N95 Pre-Use Seal-Check Adherence\u003c/h2\u003e\n \u003cp\u003eMultivariable logistic regression analysis was performed to identify independent predictors of N95 mask pre-use seal-check adherence among healthcare workers who used N95 respirators during face-to-face interactions. Several socio-demographic factors significantly influenced behavior. Healthcare workers in communal or shared living arrangements (including family, friends, or camps) were more than twice as likely to perform pre-use seal checks ( [AOR]\u0026thinsp;=\u0026thinsp;2.37; 95% CI: 1.29\u0026ndash;4.35). In contrast, older age (\u0026gt;\u0026thinsp;30 years) (AOR\u0026thinsp;=\u0026thinsp;0.57; 95% CI: 0.34\u0026ndash;0.98) and being stressed at the time of the event (AOR\u0026thinsp;=\u0026thinsp;0.46; 95% CI: 0.28\u0026ndash;0.77) were associated with significantly lower odds of adherence. Clinical exposure and institutional factors also served as strong positive predictors. Involvement in AGPs (AOR\u0026thinsp;=\u0026thinsp;1.71; 95% CI: 1.05\u0026ndash;2.80) and receipt of prior IPC training (AOR\u0026thinsp;=\u0026thinsp;1.71; 95% CI: 1.03\u0026ndash;2.83) were both associated with increased adherence. The clinical setting played a critical role, showing a clear gradient of risk. Compared to the HPDA reference group, working in GHR was associated with a 47% reduction in the likelihood of adherence (AOR\u0026thinsp;=\u0026thinsp;0.53; 95% CI: 0.30\u0026ndash;0.93). This decline was even more pronounced in MRCA, where the odds of performing seal checks dropped by 67% (AOR\u0026thinsp;=\u0026thinsp;0.33; 95% CI: 0.16\u0026ndash;0.66). The lowest level of adherence was observed in LRS, representing a 75% reduction in the odds of performing the necessary seal checks (AOR\u0026thinsp;=\u0026thinsp;0.25; 95% CI: 0.08\u0026ndash;0.77) (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eAdherence to donning and doffing procedures\u003c/h2\u003e\n \u003cp\u003eOverall, 57.0% of participants never followed recommended donning and doffing procedures, with non-adherence highest in MRCA (75.6%) and GHR (60.9%). Only 23.4% of participants consistently followed the recommended steps for more than half of interactions; adherence was highest in HPDA) (64.2%), compared to 17.6% in GHR and 9.9% in LRS. Notably, 9.7% of participants reported being unaware of the recommended steps, with unawareness most frequent in LRS (29.6%) and MRCA (10.6%). Occupational differences were striking: while 58.9% of healthcare professionals reported never following procedures, 53.2% of supporting staff reported a lack of knowledge regarding the recommended steps. Ultimately, only 24.2% of healthcare professionals and 14.9% of supporting staff followed procedures for more than half of interactions (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Multivariable logistic regression identified three factors independently associated with adherence. Involvement in AGPs was the strongest predictor; participants who performed AGPs had 3.61 times the odds of adherence compared to those who did not (AOR\u0026thinsp;=\u0026thinsp;3.61; 95% CI: 2.33\u0026ndash;5.58; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Prior IPC training was also significantly associated with adherence, with trained participants having 59% higher odds of achieving the adherence threshold (AOR\u0026thinsp;=\u0026thinsp;1.59; 95% CI: 1.05\u0026ndash;2.41; P\u0026thinsp;=\u0026thinsp;0.028). Work site was a critical determinant of adherence (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Using HPDA as the reference group (AOR\u0026thinsp;=\u0026thinsp;1.00), participants in other clinical areas showed markedly lower compliance: GHR (AOR\u0026thinsp;=\u0026thinsp;0.12; 95% CI: 0.07\u0026ndash;0.19), MRCA (AOR\u0026thinsp;=\u0026thinsp;0.08; 95% CI: 0.04\u0026ndash;0.14), and LRS (AOR\u0026thinsp;=\u0026thinsp;0.07; 95% CI: 0.03\u0026ndash;0.17), representing 88%, 92%, and 93% lower odds of adherence, respectively (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariable logistic regression analysis identifying factors associated with adherence to donning and doffing procedures during direct face to face contact among infected healthcare personnel in Addis Ababa, Ethiopia from March 2020 to March 2021(N\u0026thinsp;=\u0026thinsp;742)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\n \u003cp\u003eAdherence (\u0026ge;\u0026thinsp;50% of time)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003ecOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eAOR[95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eTotal N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eNo N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eYes N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eIPC training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e455(61.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e373(65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e82(46.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e287(38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e194(34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e93(53.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2.18[1.55\u0026ndash;3.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.59[1.05\u0026ndash;2.41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u0026thinsp;0.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eSite of work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eHPDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e159(21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e57(10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e102(58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eGHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e256(34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e211(37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e45(25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.12[0.075\u0026ndash;0.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.12 [0.07\u0026ndash;0.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMRCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e246(33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e226(39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e20(11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.05[0.03\u0026ndash;0.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.08 [0.04\u0026ndash;0.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e81(10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e73(12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e8(4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.06[0.03\u0026ndash;0.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.07 [0.03\u0026ndash;0.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eInvolved in AGP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e545(73.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e460(81.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e85(48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e197(26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e107(18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e90(51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.6 [3.16\u0026ndash;6.54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e3.6[2.3\u0026ndash;5.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e*Model diagnostics: Hosmer-Lemeshow \u0026chi;\u0026sup2; = 3.72 (P\u0026thinsp;=\u0026thinsp;0.811), ROC-AUC\u0026thinsp;=\u0026thinsp;0.823, mean VIF\u0026thinsp;=\u0026thinsp;1.23*\u003c/p\u003e\n \u003cp\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; **\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eAOR\u0026thinsp;=\u0026thinsp;adjusted odds ratio; cOR\u0026thinsp;=\u0026thinsp;crude odds ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval; HPDA\u0026thinsp;=\u0026thinsp;High-Pathogen Designated Areas; GHR\u0026thinsp;=\u0026thinsp;General High-Risk Areas; MRCA\u0026thinsp;=\u0026thinsp;Medium-Risk Clinical Areas; LRS\u0026thinsp;=\u0026thinsp;Low-Risk Support Areas; IPC\u0026thinsp;=\u0026thinsp;infection prevention and control; AGPs\u0026thinsp;=\u0026thinsp;aerosol-generating procedures\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003ePPE use during aerosolizing procedures\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eDuring AGPs, over 95% of participants used medical masks and single-use gloves for more than half of interactions. Approximately 66.5% used N95 respirators for more than half of the time. Other PPE items, including face shields, head caps, and disposable gowns, were used by only 40\u0026ndash;60% of participants. Chi-square analysis identified significant associations between work site and use of all PPE items during AGPs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all), with the exception of medical masks (P\u0026thinsp;=\u0026thinsp;0.096) and gloves (P\u0026thinsp;=\u0026thinsp;0.153). The strongest associations were found for coverall suits (Cram\u0026eacute;r\u0026apos;s V\u0026thinsp;=\u0026thinsp;0.50), shoe covers (V\u0026thinsp;=\u0026thinsp;0.44), and N95 respirators (V\u0026thinsp;=\u0026thinsp;0.41). Participants in High-Pathogen Designated Areas (HPDA) demonstrated significantly higher adherence, with over 50% usage rates for coverall suits (SR\u0026thinsp;=\u0026thinsp;6.3), shoe covers (SR\u0026thinsp;=\u0026thinsp;5.4), N95 respirators (SR\u0026thinsp;=\u0026thinsp;3.1), face shields (SR\u0026thinsp;=\u0026thinsp;4.2), and disposable gowns (SR\u0026thinsp;=\u0026thinsp;4.0). In contrast, GHR showed negative associations for use of PPE for more than half of the time, particularly for N95 respirators (SR = -1.8), coverall suits (SR = -4.0), face shields (SR = -2.8), disposable gowns (SR = -2.5), head caps (SR = -1.1), and shoe covers (SR = -3.0). No statistically significant associations were identified between PPE utilization and professional category during AGPs (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003ePPE use during interactions with body fluids\u003c/h2\u003e\n \u003cp\u003eDuring exposure to patient body fluids, 97.7% of participants used medical masks and 93.4% used gloves for more than half of the time. However, only 40.2% used N95 respirators for more than half of the time, with notably higher utilization in HPDA settings. Regarding additional protective equipment, many participants never used face shields (50.8%), disposable gowns (53.8%), coverall suits (65.4%), head caps (53.2%), or shoe covers (70.1%) during contact with body fluids. Non-utilization of these items decreased progressively from HPDA to non-designated areas. Utilization patterns revealed significant site-specific variations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all), with the exception of medical masks and gloves. HPDA settings exhibited the highest compliance rates (\u0026ge;\u0026thinsp;50%), while MRCA showed markedly lower utilization. Healthcare professionals demonstrated significantly higher compliance than supporting staff, particularly for medical masks (98.0% vs. 66.7%; P\u0026thinsp;=\u0026thinsp;0.001, V\u0026thinsp;=\u0026thinsp;0.46) and gloves (94.9% vs. 16.7%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, V\u0026thinsp;=\u0026thinsp;0.58). The disparity was most pronounced for gloves, where adherence among supporting staff was only 16.7% compared to 94.9% among healthcare professionals. While trends suggested lower utilization of gowns, face shields, and coverall suits among supporting staff, these differences did not reach statistical significance (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdherence to personal protective equipment utilization during aerosol generating procedures and contact with body fluids stratified by professional role and clinical setting in Addis Ababa, Ethiopia from March 2020 to March 2021\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eExposure Modality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eType of\u003c/p\u003e\n \u003cp\u003ePPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003ePPE\u003c/p\u003e\n \u003cp\u003euse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e\n \u003cp\u003ePlace of work before the diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\n \u003cp\u003eOccupation of the HCWs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003eHPDA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003eGHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(V)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value (V)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e% (SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e% (SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e% (SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e% (SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e% (SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e% (SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"23\" rowspan=\"24\"\u003e\n \u003cp\u003ePPE\u003c/p\u003e\n \u003cp\u003euse\u003c/p\u003e\n \u003cp\u003eduring\u003c/p\u003e\n \u003cp\u003eAGP\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eMedical mask\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.9(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.0(-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e7.1(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.096\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e1.5(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;1.000\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.9(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.0(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.0(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e1.0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e192(97.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e94.2(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e100(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e100.0(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e92.9(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e97.4(-0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e100.0(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003cp\u003euse\u003c/p\u003e\n \u003cp\u003egloves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.4(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.1(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e7.1(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.153\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e1.5(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;1.000\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.1(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.0(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e0.5(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e193(98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e98.6(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e97.9(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e100.0(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e92.9(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e97.9(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e100.0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eN-95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e51(25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.4(-4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e41.5(3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e30.0(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e35.7(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e25.8(-0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e33.3(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;1.000\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e15(7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.4(-1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.4(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e35.0(4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.0(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e7.7(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e131(66.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e97.1(3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e51.1(-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e35.0(-1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e64.3(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e66.5(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e66.7(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eFace shields/ googles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e73(37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e5.8(-4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e54.3(2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e55.0(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e50.0(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e36.6(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e66.7(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;1.000\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e20(10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4.3(-1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e13.8(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e15.0(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e7.1(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e10.3(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e104(52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e89.9(4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e31.9(-2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e30.0(-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e42.9(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e53.1(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e33.3(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eGowns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e91(46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e18.8(-3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e60.6(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e70.0(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e50.0(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e45.9(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e66.7(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;1.000\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e20(10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e5.8(-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e12.8(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e15.0(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e7.1(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e10.3(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e86(43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e75.4(4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e26.6(-2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e15.0(-1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e42.9(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e43.8(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e33.3(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eCoverall\u003c/p\u003e\n \u003cp\u003esuit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e111(56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e15.9(-4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e76.6(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e95.0(2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e64.3(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e56.2(-0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e66.7(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;1.000\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e14(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.4(-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e11.7(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e5.0(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e7.1(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e7.2(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e72(36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e82.6(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e11.7(-4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0(-2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e28.6(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e36.6(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e33.3(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eHead cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e73(37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e11.6(-3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e45.7(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e70.0(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e57.1(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e36.6(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e66.7(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.625\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e10(5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4.3(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.3(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e5.0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e7.1(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e5.2(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e114(57.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e84.1(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e48.9(-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e25.0(-1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e35.7(-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e58.2(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e33.3(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eShoe\u003c/p\u003e\n \u003cp\u003ecovers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e126(64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e24.6(-4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e81.9(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e100.0(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e85.7(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e63.9(-0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e66.7(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;1.000\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e18(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e14.5(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.4(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0(-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e7.1(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e9.3(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e53(26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e60.9(5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e10.6(-3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0(-2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e7.1(-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e26.8(-0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e33.3(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"23\" rowspan=\"24\"\u003e\n \u003cp\u003ePPE\u003c/p\u003e\n \u003cp\u003eUse\u003c/p\u003e\n \u003cp\u003eduring contact\u003c/p\u003e\n \u003cp\u003ewith\u003c/p\u003e\n \u003cp\u003epatient body\u003c/p\u003e\n \u003cp\u003efluids\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;301)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eMedical mask\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.4(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.0(-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.4(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.0(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.081\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e1.4(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3.6(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.0(-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.0(-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.0(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e0.3(-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e33.3(7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e294(97.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e94.0(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e100.0(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e97.6(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e100.0(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e98.3(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e66.7(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003cp\u003euse\u003c/p\u003e\n \u003cp\u003egloves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e12(4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.4(-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3.6(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4.8(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e8.7(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.189\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e2.4(-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e83.3(9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8(2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0(-1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2.7(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e6.0(1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.0(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e2.7(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e281(93.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e97.6(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e93.8(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e89.2(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e91.3(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e94.9(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e16.7(-1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eN-95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e143(47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e21.7(-3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e51.8(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e66.3(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e52.2(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e46.8(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e83.3(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.332\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e37(12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3.6(-2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e11.6(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e21.7(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e13.0(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e12.5(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e121(40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e74.7(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e36.6(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e12.0(-4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e34.8(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e40.7(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e16.7(-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eFace shields/\u003c/p\u003e\n \u003cp\u003egoogles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e153(50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e13.3(-4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e66.1(2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e69.9(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e43.5(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e50.2(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e83.3(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.408\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e37(12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6.0(-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e10.7(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e15.7(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e30.4(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e12.5(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e111(36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e80.7(6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e23.2(-2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e14.5(-3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e26.1(-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e37.3(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e16.7(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eGowns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e162(53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e20.5(-4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e67.9(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e71.1(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e43.5(-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e52.9(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e100.0(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.088\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e27(9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3.6(-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.9(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e12.0(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e17.4(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e9.2(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e112(37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e75.9(5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e23.2(-2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e16.9(-3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e39.1(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e38.0(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eCoverall\u003c/p\u003e\n \u003cp\u003esuit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e197(65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e20.5(-5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e81.3(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e88.0(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e69.6(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e65.1(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e83.3(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.774\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e18(6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3.6(-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.1(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e6.0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e8.7(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e6.1(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e86(28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e75.9(8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e11.6(-3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e6.0(-3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e21.7(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e28.8(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e16.7(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eHead cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e160(53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16.9(-4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e56.3(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e79.5(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e73.9(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e52.5(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e83.3(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.463\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e17(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e8.4(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.5(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e3.6(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e8.7(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e5.8(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e124(41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e74.7(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e39.3(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e16.9(-3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e17.4(-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e41.7(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e16.7(-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eShoe covers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e211(70.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e27.7(-4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e85.7(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e89.2(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e78.3(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e69.8(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e83.3(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;1.000\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e25(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e19.3(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.4(-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.4(-1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e4.3(-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e8.5(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.0(-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e65(21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e53.0(6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.9(-2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e8.4(-2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e17.4(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e21.7(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e16.7(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eSR= standardized residuals; High-Pathogen Designated Areas (HPDA); General High-Risk Areas (GHR) ; Medium-Risk Clinical Areas (MRCA) ;Low-Risk Support Areas (LRS); HCPs=health care professionals; SS= supporting staff; AGE: Aerosol Generating Procedures. Statistical significance (P-values) and effect sizes (Cram\u0026eacute;r\u0026apos;s V) are included to illustrate the strength of the association between workplace designation and protocol compliance\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePPE Use during Contact with Patient Materials and Surfaces\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eDuring interactions with patient materials and surrounding surfaces, medical masks were the most consistently used PPE. Adherence for mask use during material handling and surface contact was 89.1% and 85.7%, respectively, with utilization increasing significantly from LRS to HPDA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, V\u0026thinsp;=\u0026thinsp;0.14). Single-use gloves were the second most common PPE, used by 73.5% of participants during material interactions and 63.1% during surface contact. Glove utilization in LRS was approximately 31\u0026ndash;32%, rising sharply to over 89% in HPDA settings (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, V\u0026thinsp;=\u0026thinsp;0.27\u0026ndash;0.28) .Specialized respiratory protection remained low across both environmental domains, with only 32.2% of participants using N95 respirators during material interactions and 38.1% during surface contact. However, utilization was consistently higher in HPDA zones (up to 80.2% ) compared to non-specialized areas (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, V\u0026thinsp;=\u0026thinsp;0.37\u0026ndash;0.40). Other PPE items, including face shields, disposable gowns, coverall suits, head caps, and shoe covers, were used by only 10\u0026ndash;30% of participants for more than half of interactions, with even lower adherence in non-designated areas.\u003c/p\u003e\n \u003cp\u003eChi-square analysis revealed significant associations between work site and utilization of all PPE items (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all), with very strong effect sizes for coverall suits (V\u0026thinsp;=\u0026thinsp;0.47\u0026ndash;0.50), face shields (V\u0026thinsp;=\u0026thinsp;0.44\u0026ndash;0.46), and shoe covers (V\u0026thinsp;=\u0026thinsp;0.37\u0026ndash;0.42). Participants in HPDA settings demonstrated significantly higher adherence rates (\u0026ge;\u0026thinsp;50%), characterized by strong positive standardized residuals for coverall suits (SR\u0026thinsp;=\u0026thinsp;13.0 for materials, 11.6 for surfaces), face shields (SR\u0026thinsp;=\u0026thinsp;11.7 for materials, 10.2 for surfaces), and disposable gowns (SR\u0026thinsp;=\u0026thinsp;10.1 for materials). Regarding professional categories, healthcare professionals generally reported higher PPE utilization than supporting staff. Significant associations were noted for medical masks and gloves during handling of patient materials (P\u0026thinsp;=\u0026thinsp;0.028, V\u0026thinsp;=\u0026thinsp;0.10 and P\u0026thinsp;=\u0026thinsp;0.013, V\u0026thinsp;=\u0026thinsp;0.12, respectively). Coverall suit usage during surface contact showed a significant difference by occupation (P\u0026thinsp;=\u0026thinsp;0.003, Cram\u0026eacute;r\u0026apos;s V\u0026thinsp;=\u0026thinsp;0.168). No significant associations were found between professional category and the use of other PPE items during these environmental interactions (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) .\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdherence to personal protective equipment utilization during contact with patient materials and contaminated surfaces stratified by clinical setting and professional role in Addis Ababa, Ethiopia from March 2020 to March 2021\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eExposure Modality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eType of PPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003ePPE use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\n \u003cp\u003ePlace of work before the diagnosis of COVID 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003cp\u003e(v)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003eHPDA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003eGHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003eHCPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eP value(v)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e%(SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e%(SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e%(SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e%(SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e%(SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e%(SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"23\" rowspan=\"24\"\u003e\n \u003cp\u003ePPE\u003c/p\u003e\n \u003cp\u003euse\u003c/p\u003e\n \u003cp\u003eduring\u003c/p\u003e\n \u003cp\u003econtact\u003c/p\u003e\n \u003cp\u003ewith\u003c/p\u003e\n \u003cp\u003epatient\u003c/p\u003e\n \u003cp\u003ematerials\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;631)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eMedical mask\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e39(6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.8(-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3.2(-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e8.2(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e18.3(3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e5.9(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e9.8(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.028\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e30(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.1(-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.1(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4.3(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e15.0(3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e4.2(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e12.2(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e562(89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e95.0(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e92.8(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e87.5(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e66.7(-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e89.8(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e78.0(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003cp\u003euse\u003c/p\u003e\n \u003cp\u003egloves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e117(18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6.4(-3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e13.1(-1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e21.6(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e56.7(6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e17.3(-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e36.6(2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.013\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e50(7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4.3(-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.9(-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e11.5(1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e11.7(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e8.1(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e4.9(-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e464(73.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e89.4(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e81.1(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e66.8(-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e31.7(-3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e74.6(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e58.5(-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eN-95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e343(54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16.3(-6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e55.4(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e72.1(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e78.3(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e53.7(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e63.4(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.195\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e85(13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e6.4(-2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e15.8(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e15.4(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e15.0(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e13.2(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e17.1(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e203(32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e77.3(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e28.8(-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e12.5(-5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e6.7(-3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e33.1(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e19.5(-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eFace\u003c/p\u003e\n \u003cp\u003eshields/ googles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e418(66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e21.3(-6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e74.8(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e82.7(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e83.3(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e66.1(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e68.3(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.772\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e66(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e7.8(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e13.5(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e8.2(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e13.3(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e10.3(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e12.2(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e147(23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e70.9(11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e11.7(-3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e9.1(-4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e3.3(-3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e23.6(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e19.5(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eGowns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e444(70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e31.2(-5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e76.6(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e86.1(2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e85.0(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e70.5(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e68.3(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.697\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e56(8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e9.2(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e11.7(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4.3(-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e13.3(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e8.6(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e12.2(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e131(20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e59.6(10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e11.7(-3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e9.6(-3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.7(-3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e20.8(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e19.5(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eCoverall\u003c/p\u003e\n \u003cp\u003esuit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e488(77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e31.9(-6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e86.0(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e94.7(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e91.7(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e77.5(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e75.6(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.092\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e43(6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e8.5(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.6(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e3.8(-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e6.7(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e6.3(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e14.6(1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e100(15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e59.6(13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.4(-3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.4(-5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1.7(-2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e16.3(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e9.8(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eHead cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e404(64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e27.7(-5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e68.9(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e77.4(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e85.0(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e63.4(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e73.2(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.508\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e40(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e7.1(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.9(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e5.3(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e10.0(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e6.4(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e4.9(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e187(29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e65.2(7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e25.2(-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e17.3(-3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e5.0(-3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e30.2(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e22.0(-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eShoe\u003c/p\u003e\n \u003cp\u003ecovers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e515(81.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e46.8(-4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e88.3(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e94.7(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e93.3(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e81.5(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e82.9(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;1.000\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e47(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e14.2(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.2(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e3.4(-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e6.7(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e7.5(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e7.3(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e69(10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e39.0(10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.5(-2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.9(-3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.0(-2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e11.0(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e9.8(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"23\" rowspan=\"24\"\u003e\n \u003cp\u003ePPE\u003c/p\u003e\n \u003cp\u003euse\u003c/p\u003e\n \u003cp\u003eduring\u003c/p\u003e\n \u003cp\u003econtact\u003c/p\u003e\n \u003cp\u003ewith\u003c/p\u003e\n \u003cp\u003epatient\u003c/p\u003e\n \u003cp\u003esurfaces\u003c/p\u003e\n \u003cp\u003earound\u003c/p\u003e\n \u003cp\u003epatients\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;491)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eMedical mask\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e47(9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4.3(-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.7(-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e11.8(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e29.2(4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e9.5(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e11.1(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.431\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e23(4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3.4(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.6(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e5.2(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e6.3(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e4.4(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e8.3(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e421(85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e92.2(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e89.7(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e83.0(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e64.6(-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e86.2(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e80.6(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003cp\u003euse gloves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e145(29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4.3(-5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e26.4(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e41.2(2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e64.6(4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e28.4(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e44.4(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.136\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e36(7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4.3(-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e6.9(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e11.1(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e4.2(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e7.5(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e5.6(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e310(63.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e91.4(3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e66.7(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e47.7(-2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e31.3(-2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e64.2(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e50.0(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eN-95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e234(47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e12.1(-5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e50.0(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e64.7(3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e70.8(2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e47.0(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e55.6(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.207\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e70(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e7.8(-1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e16.7(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e17.6(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e10.4(-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e13.8(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e19.4(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e187(38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e80.2(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e33.3(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e17.6(-4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e18.8(-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e39.1(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e25.0(-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eFace\u003c/p\u003e\n \u003cp\u003eshields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e316(64.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e15.5(-6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e76.4(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e82.4(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e81.3(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e64.2(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e66.7(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.300\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e52(10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e12.1(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e11.5(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e8.5(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e10.4(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e10.1(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e16.7(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e123(25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e72.4(10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e12.1(-3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e9.2(-3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e8.3(-2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e25.7(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e16.7(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eGowns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e345(70.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e23.3(-6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e81.6(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e88.9(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e83.3(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e70.1(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e72.2(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.137\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e45(9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e13.8(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.6(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e5.9(-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e10.4(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e8.6(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e16.7(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e101(20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e62.9(10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e9.8(-3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e5.2(-4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e6.3(-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e21.3(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e11.1(-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eCoverall\u003c/p\u003e\n \u003cp\u003esuit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e373(76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e25.9(-6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e89.1(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e94.1(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e91.7(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e76.0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e75.0(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.003\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e31(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e11.2(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.2(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e3.9(-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e6.3(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e5.3(-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e19.4(3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e87(17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e62.9(11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e5.7(-3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.0(-4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e2.1(-2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e18.7(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e5.6(-1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eHead cap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e312(63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e19.0(-6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e71.3(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e81.0(2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e87.5(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e62.6(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e75.0(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.186\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e34(6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e12.1(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.6(-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e5.9(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e6.3(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e6.8(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e8.3(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e145(29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e69.0(7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e24.1(-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e13.1(-3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e6.3(-3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e30.5(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e16.7(-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eShoe\u003c/p\u003e\n \u003cp\u003ecovers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e389(79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e37.1(-5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e92.5(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e92.8(1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e89.6(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e79.1(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e80.6(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c12\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.275\u003c/p\u003e\n \u003cp\u003eV\u0026thinsp;=\u0026thinsp;0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e42(8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e21.6(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.6(-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e3.9(-2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e6.3(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e8.1(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e13.9(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e60(12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e41.4(9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2.9(-3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e3.3(-3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e4.2(-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e12.7(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e5.6(-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eSR= standardized residuals; High-Pathogen Designated Areas (HPDA); General High-Risk Areas (GHR) ; Medium-Risk Clinical Areas (MRCA) ;Low-Risk Support Areas (LRS); HCPs=health care professionals; SS= supporting staff. Statistical significance (P-values) and effect sizes (Cram\u0026eacute;r\u0026apos;s V) are included to illustrate the strength of the association between workplace designation and protocol compliance\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eHand hygiene practices of infected HCWs\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eBefore direct face-to-face patient contact, 49.3% of participants practiced hand hygiene for more than half of interactions. Adherence increased to 72.6% following direct patient contact and reached 82.5% after contact with environmental surfaces. Comparative analysis revealed a consistent increase in hand hygiene compliance from LRS to HPDA. Compliance rates for performing hand hygiene for more than half of interactions improved from 32.1% to 62.9% before direct patient contact, from 51.9% to 81.1% after direct contact, and from 60.4% to 88.8% after surface contact. After contact with patient body fluids, 87.7% of participants adhered to hand hygiene practices. These rates remained notably high across all work sites: 91.3% in LRS, 84.3% in MRCA, 89.3% in GHR, and 88.0% in HPDA. Chi-square analysis revealed significant associations between hand hygiene practices and work site (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for most interactions), with the notable exception of hand hygiene following contact with body fluids (P\u0026thinsp;=\u0026thinsp;0.215). Participants in HPDA demonstrated the highest adherence before direct patient contact ( [SR]\u0026thinsp;=\u0026thinsp;2.4). Conversely, staff in LRS were significantly more likely to report never performing hand hygiene before direct contact (SR\u0026thinsp;=\u0026thinsp;3.3), after direct contact (SR\u0026thinsp;=\u0026thinsp;4.8), or after exposure to body fluids (SR\u0026thinsp;=\u0026thinsp;3.7) .\u003c/p\u003e\n \u003cp\u003eA significant disparity in hand hygiene practices was identified between healthcare professionals and supporting staff. Before face-to-face contact, 50.6% of healthcare professionals adhered to hand hygiene protocols for more than half of interactions, compared to 29.8% of supporting staff. This gap was most pronounced after exposure to body fluids, with compliance rates of 89.2% for healthcare professionals versus 16.7% for supporting staff. Supporting staff were nearly 3.5 times more likely than professionals to never perform hand hygiene after direct contact (42.6% vs. 12.4%; SR\u0026thinsp;=\u0026thinsp;5.1). Statistical analysis confirmed significant differences in compliance across all interactions (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all). Supporting staff were significantly more likely to report never performing hand hygiene before contact (SR\u0026thinsp;=\u0026thinsp;2.9), after contact (SR\u0026thinsp;=\u0026thinsp;5.1), following exposure to body fluids (SR\u0026thinsp;=\u0026thinsp;1.5), and after surface contact (SR\u0026thinsp;=\u0026thinsp;2.7). Alcohol-based hand rub was the most common method used across all patient interactions (Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) .\u0026nbsp;\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdherence to hand hygiene practices among healthcare personnel stratified by clinical setting and professional role in Addis Ababa, Ethiopia from March 2020 to March 2021\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eHand hygiene practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\n \u003cp\u003ePlace of work before the diagnosis of COVID 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003eHPDA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003eGHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCPs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e%(SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e%(SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e% (SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e% (SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e% (SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e% (SR)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eHand hygiene practice before direct face to face contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e212 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e15.7 (-3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e24.6 (-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e34.6 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e48.1(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e27.1(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e51.1(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e164(22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e21.4 (-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e23.4 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e22.0 (-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e19.8 (-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e22.3(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e19.1(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e366 (49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e62.9 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e52.0(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e43.5 (-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e32.1(-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e50.6(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e29.8(-1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.002 (V\u0026thinsp;=\u0026thinsp;0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eMaterial used during hand hygiene practice before direct face to face contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e449 (84.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e85.8 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e81.3(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e88.2 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e83.3(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e85.0(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e78.3(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSoap and water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e77 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e14.2 (-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e18.1(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e10.6(-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e14.3(-0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e14.2(-0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e21.7(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.0(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.5(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.2(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.4(1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.8(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e0.0(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.231 (V\u0026thinsp;=\u0026thinsp;0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.465 (V\u0026thinsp;=\u0026thinsp;0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eHand hygiene practice after direct face to face contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e106 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e11.3 (-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e8.6 (-2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e15.4(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e34.6(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e12.4(-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e42.6(5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e97 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e7.5 (-1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e14.8 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e14.6(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e13.6(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e13.1(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e12.8(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e539 (72.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e81.1(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e76.6 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e69.9(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e51.9(-2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e74.5(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e44.7(-2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eMaterial used during hand hygiene practice after direct face to face contact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e485 (76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e74.3(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e78.2 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e76.8(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e73.6(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e76.8(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e70.4(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSoap and water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e146 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e25.7(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e21.8 (-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e22.2(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e24.5(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e22.7(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e29.6(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.0(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.0 (-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.0(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.9(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.5(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e0.0(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.421(V\u0026thinsp;=\u0026thinsp;0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.465(V\u0026thinsp;=\u0026thinsp;0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eHand hygiene practice after contact with body fluids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.4(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.8 (-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e9.6(2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4.3(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e4.1(-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e16.7(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e24 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9.6(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e8.9(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e6.0(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e4.3(-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e6.8(-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e66.7(5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u0026gt;=50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e264 (87.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e88.0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e89.3(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e84.3(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e91.3(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e89.2(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e16.7(-1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.215 (V\u0026thinsp;=\u0026thinsp;0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eMaterial used during hand hygiene practice after contact with body fluids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e206 (71.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e69.1(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e70.9(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e73.3(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e77.3(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e71.4(-0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e80.0(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSoap and water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e82 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e30.9(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e29.1(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e26.7(-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e22.7(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e28.6(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e20.0(-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.867 (V\u0026thinsp;=\u0026thinsp;0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;1.000 (V= -0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eHand hygiene practice after contact with surfaces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e86(17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e11.2(-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e12.6(-1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e20.9(1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e39.6(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e16.0(-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e36.1(2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e405 (82.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e88.8(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e87.4(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e79.1(-0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e60.4(-1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e84.0(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e63.9(-1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V\u0026thinsp;=\u0026thinsp;0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\n \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (V =-0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eMaterial used during hand hygiene practice after contact with surfaces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e323 (79.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e73.8(-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e83.6(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e84.3(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e62.1(-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e79.8(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e78.3(-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eSoap and water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e82 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e26.2(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16.4(-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e15.7(-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e37.9(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e20.2(-0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e21.7(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e𝜒2( Cram\u0026eacute;r\u0026apos;s V )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.013 (V\u0026thinsp;=\u0026thinsp;0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\n \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.793 (V\u0026thinsp;=\u0026thinsp;0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eSR= standardized residuals; High-Pathogen Designated Areas (HPDA); General High-Risk Areas (GHR) ; Medium-Risk Clinical Areas (MRCA) ;Low-Risk Support Areas (LRS); HCPs=health care professionals; SS= supporting staff. Statistical significance (P-values) and effect sizes (Cramer\u0026apos;s V) are included to illustrate the strength of the association between workplace designation and protocol compliance\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated IPC adherence among COVID-19–infected HCWs during the 14 days preceding their diagnosis. Key findings included suboptimal use of fluid-resistant PPE, reactive hand hygiene patterns (higher after contact than before), and significant gaps in N95 seal-checking, fit testing, and donning/doffing procedures. A consistent gradient of adherence was observed, with superior practices in HPDA compared to non-designated areas, and among healthcare professionals compared to supporting staff. After multivariable logistic regression, among HCWs with direct face-to-face contact (N = 742), donning and doffing adherence was significantly associated with work site, IPC training, professional category, and involvement in AGPs. In contrast, among those who used N95 respirators during face-to-face contact (N = 350), pre-use seal-check adherence was associated with work site, age (\u0026gt;30 years), stress at the time of event, communal living, IPC training, and involvement in AGPs, with work site emerging as the strongest predictor.\u003c/p\u003e\n\u003cp\u003eA significant majority of study participants (80.8 percent) were working in clinical areas not specifically designated for infectious disease care, indicating that the primary risk of infection resided within the general hospital environment rather than high-containment units [25]. This high prevalence in non-designated zones suggests a critical \"protection gap\" where a lower perceived risk likely led to relaxed IPC vigilance and suboptimal adherence compared to the high-threat environment of dedicated treatment wards [26] . Furthermore, the infection rate in low risk areas and among supporting staffs highlights a vulnerability to horizontal transmission in shared, non-clinical spaces where standard precautions are frequently deprioritized [26,27].\u003c/p\u003e\n\u003ch3\u003eMask Adherence\u003c/h3\u003e\n\u003cp\u003eMask adherence was high (85.7–97.7%), aligning with facility-based findings in Eastern Ethiopia (88.3%) and Addis Ababa (85.7–93%)\u0026nbsp;[28–30].\u0026nbsp;Conversely, our results exceed utilization rates reported in Northern Shewa (27.4%) and Northeastern Ethiopia (50.1%)\u0026nbsp;(25) (26),\u0026nbsp;a discrepancy that may reflect higher perceived risk and better resource availability in urban centers during the pandemic peak.\u0026nbsp;Healthcare professionals demonstrated significantly higher mask adherence than supporting staff, a trend mirrored in previous research within police health facilities in Addis Ababa\u0026nbsp;[33].\u0026nbsp;A clear spatial gradient in compliance emerged: HCWs in HPDA maintained significantly higher utilization rates than those in non-designated areas, a pattern documented in Uganda, Germany, China, and Ghana\u0026nbsp;[34–37]. The presence of infection among these workers, notwithstanding high mask use, may suggest relaxation of protocols in staff-only areas facilitating peer-to-peer transmission, or potential social desirability bias where self-reported compliance is inflated compared to actual practice\u0026nbsp;[38]\u0026nbsp;[39].\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eGlove Adherence\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eGlove adherence was high during AGPs (98.0%) but lower during surface contact (63.1%). While these rates exceed those reported in Uganda and Southwest Ethiopia [34] [40], they remain lower than the near-universal compliance observed in Burkina Faso, Saudi Arabia, Pakistan, and China [37,41–43] . \u0026nbsp;A clear spatial gradient was evident, with significantly higher adherence among HCWs in HPDA compared to non-designated areas, mirroring findings from Uganda and Ghana \u0026nbsp;[34] [41]. \u0026nbsp;This disparity may be associated with risk perception; HCWs may maintain rigorous IPC measures when perceiving a direct, immediate threat to personal safety, such as during AGPs or within high-containment units [44]. Notably, the gap between healthcare professionals and supporting staff is wide in terms of glove utilization during interactions with body fluids and patient materials, indicating that supporting staff, who often handle infectious waste or contaminated linens, may represent the least protected group during high-risk fluid exposure. Such professional disparities may reflect inequities in safety training and the systemic exclusion of non-clinical staff from formal IPC orientation. Conversely, lower compliance during surface contact may reflect habituation to low-threat tasks or failure to recognize the environment as a viable transmission vehicle\u0026nbsp;[45].\u003c/p\u003e\n\u003ch3\u003eN95 Respirator Utilization and Fit Testing\u003c/h3\u003e\n\u003cp\u003eN95 use was high during AGPs (66.5%) but low during face-to-face contact (\u0026lt;40%), suggesting that HCWs prioritize N95 use for the most visible risks while reverting to medical masks for routine care. While our utilization rate exceeds reports from Nigeria (\u0026lt;10%) and aligns with previous findings in Addis Ababa (21.2%), it remains suboptimal for comprehensive protection [29] [46]. A critical finding was the near-total absence of N95 fit testing, with 98.9% of HCWs indicating their respirators were never tested, a rate significantly lower than those reported in Qatar and Australia [47] [48]. This suggests a profound institutional infrastructure gap in Ethiopia, where a \"one-size-fits-all\" procurement approach, combined with a lack of specialized testing kits, renders formal fit testing a systemic impossibility. Furthermore, 65% of respondents failed to perform a pre-use seal check, a failure rate notably higher than reported in Nepal \u0026nbsp;[49]. The widespread absence of both professional fit testing and individual seal checks results in compromised safety margins, as a poorly fitted N95 respirator may offer no more functional defense than a standard surgical mask against submicron particles\u0026nbsp;[50,51], and the superior filtration capacity of the respirator may be bypassed by peripheral leakage\u0026nbsp;[52].\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eDeterminants of Seal-Check Adherence\u003c/h3\u003e\n\u003cp\u003eMultivariable analysis identified work site, age, stress at the time of the event, communal living, IPC training, and involvement in AGPs as significant predictors of pre-use seal-check adherence, with work site emerging as the primary determinant. HCWs in HPDA demonstrated superior compliance, likely due to a heightened safety culture and peer supervision, whereas those in GHR areas appeared to operate under a \"false sense of security.\" This suggest that environmental context may override procedural necessity, leading to a relaxation of WHO-recommended protocols in general wards despite identical biological risks [53]. Furthermore, HCWs aged more than 30 years had lower odds of adherence, potentially reflecting generational gaps in recent pandemic-related training or lower risk perception compared to younger cohorts or a greater awareness of evolving respiratory protection guidelines. Adherence was also significantly compromised by stress at the time of the event, which likely impairs the cognitive function and attention to detail required for safety protocols. This finding suggests that institutional interventions, including mental health support, are critical for indirectly bolstering IPC adherence [54]. Conversely, HCWs in communal or family living arrangements were more than twice as likely to perform seal checks, likely driven by \"altruistic protection\" and a heightened desire to safeguard household members. Finally, the positive associations with IPC training and AGP involvement highlight the efficacy of targeted institutional interventions. Involvement in high-risk procedures like AGPs likely reinforces the perceived severity of exposure, leading to better safety hygiene. Our results suggest that institutional safety culture must move beyond specialized training to address the environmental and psychological barriers, such as stress and perceived area-specific risk that hinder universal N95 seal check compliance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDonning and Doffing Adherence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eErrors in donning and doffing PPE lead to autoinoculation and pathogen transmission to HCWs\u0026nbsp;[55]\u0026nbsp;[56].\u0026nbsp;Our descriptive data revealed a knowledge‑behavior gap across professional roles, though professional category was not significant after multivariable adjustment. Supporting staff exhibited a knowledge gap (53.1% unaware of steps), whereas HCPs showed a behavioral gap (7.3% unaware but 56.8% never followed procedures), suggesting HCPs need implementation support and supporting staff require basic education. These descriptive differences align with studies from Nepal and Saudi Arabia linking profession and workplace to knowledge\u0026nbsp;[57]\u0026nbsp;[58].\u003c/p\u003e\n\u003cp\u003eA stark contrast in adherence emerged between designated infectious units and non‑designated wards, with proper technique declining significantly as perceived risk moved away from specialized units—rates lower than those reported in Saudi Arabia, India, and Canada\u0026nbsp;[59–61]. Multivariable analysis confirmed work site as the strongest driver of donning/doffing adherence. Compared to HPDA, HCWs in non‑designated areas had markedly lower odds: GHR (AOR 0.12 [0.07–0.19]), MRCA (AOR 0.08 [0.04–0.14]), and LRS (AOR 0.07 [0.03–0.17]), representing 88–93% lower adherence. This steep gradient likely reflects the cognitive demands of doffing, which often occurs at shift end when staff are exhausted, despite being the period of highest self‑contamination risk\u0026nbsp;[62]. Superior adherence in designated units aligns with evidence that specialized wards maintain more rigorous technical culture and supervision\u0026nbsp;[36]\u0026nbsp;. Involvement in AGP (AOR 3.61 [2.33–5.58]) and prior IPC training (AOR 1.59 [1.05–2.41]) were also significant predictors, supporting the premise that risk perception drives protective behaviors [63].\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eEye Protection\u003c/h3\u003e\n\u003cp\u003eSystematic reviews have established that mandatory eye protection, particularly face shields, significantly reduces infection risk among HCWs [64]. Despite this, eye protection utilization was low (23–52.8%) across interaction types. Adherence was highest during AGPs and direct face-to-face contact but dropped during tasks involving body fluid exposure, surface decontamination, or handling of contaminated materials. This pattern suggests that HCWs may prioritize eye protection only when splash or inhalation threats are immediately visible, rather than maintaining it as a universal precaution. These low rates align with findings from the South Wollo Zone (31.9%) and Addis Ababa (19%), Ethiopia\u0026nbsp;[29]\u0026nbsp;[31]. While suboptimal adherence has been noted in Burkina Faso, Saudi Arabia, and Pakistan (1.56%, 68%, and 45%, respectively), our results stand in sharp contrast to the 100% compliance reported among Korean frontline nurses\u0026nbsp;[41–43]\u0026nbsp;[65]. This low utilization may stem from significant practical barriers documented in previous literature; HCWs frequently report that goggles or shields impair visibility due to fogging, while heat and dehydration issues reported by up to 76% of participants in similar settings hinder sustained use\u0026nbsp;[66]. Furthermore, the tendency to omit eye protection during surface or material handling suggests a failure to recognize the environment as a viable transmission vehicle. This inconsistent approach, driven by perceived rather than actual risk, likely contributes to the vulnerability of staff working in non-specialized areas where standard precautions are frequently deprioritized\u0026nbsp;[67].\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eFluid-Resistant PPE\u003c/h3\u003e\n\u003cp\u003eUtilization of disposable gowns, coverall suits, head caps, and shoe covers remained below 50% across most patient interactions, although rates increased progressively from non-designated areas to HPDA. This low adherence mirrors a previous Addis Ababa study reporting hair protection and gown use at 18% and 72.4%, respectively [30], and aligns with findings from Southwest Ethiopia (39.8%) [40]. However, these results contrast sharply with significantly higher gown utilization reported in Korea (98%), Burkina Faso (95.6%), Pakistan (90%), and Saudi Arabia (85%) [41–43] [65]. This disparity might be attributed to systemic resource constraints in Ethiopia, where a lack of medical supplies persists as a primary barrier to universal precaution adherence, even among HCWs with strong conceptual understanding of PPE [68].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring body fluid contact, a massive precaution gap exists: while mask and glove use reached 97.7%, over 70% of staff omitted shoe covers and over 50% failed to use gowns or shields. Behavior was dictated by work site rather than objective biological risk; HCWs adhered strictly to protocols in HPDA but relaxed standards in MRCA or LRS zones, evidenced by extreme standardized residuals for face shields (SR = 11.7) and coveralls (SR = 13.0). This site-dependent safety culture persists despite high infection rates in non-specialized areas, proving that materials from GHR and MRCA zones remain highly infectious. Furthermore, support staff handling heavy contamination exhibited significantly lower adherence than clinicians (P \u0026lt; 0.001), being less likely to use gloves (58.5% vs. 74.6%, P = 0.013). This indicates that risk perception is frequently tethered to professional status and departmental labels rather than the objective presence of pathogens.\u003c/p\u003e\n\u003ch3\u003eHand Hygiene practice\u003c/h3\u003e\n\u003cp\u003eHand hygiene adherence was reactive, ranging from 49.3% before face-to-face contact to 82.5% after contact with body fluids. Studies from Nigeria and India reported superior adherence compared to our results and a similar study from Greece [69–71]. A multicenter study from Ethiopia documented an 81.4% compliance rate \u0026nbsp;during the pandemic [72], while a recent meta-analysis revealed a lower 38% pooled compliance among Ethiopian HCWs, though a subgroup analysis showed a higher rate of 73% within Addis Ababa [31]. Global trends reflect a significant increase in hand hygiene since the pandemic began, with a recent review reporting a 74% overall compliance rate compared to pre pandemic levels which ranged from 5% to 89% [73]. In our study, adherence was highest (\u0026gt;80%) following contact with body fluids or contaminated surfaces. Our finding that adherence was highest (\u0026gt;80%) following contact with body fluids aligns with meta-analytical evidence showing that HCWs prioritize hand hygiene after body fluid exposure [73] ; our study reinforces this with a drop to 49.3% prior to contact compared to 74.4% after interactions. This disparity suggests that hand hygiene may be driven by a desire for self-protection rather than patient safety, as adherence increases when the perceived risk of acquiring infection is highest\u0026nbsp;[74]\u0026nbsp;\u0026nbsp;[75]. Such reactive adherence patterns create a significant window for healthcare associated infections, as the most critical step for preventing cross contamination, hand hygiene prior to contact, remains the least practiced.\u003c/p\u003e\n\u003cp\u003eHPDA consistently demonstrated better hand hygiene compliance than non-designated areas, a finding supported by research from university hospitals in Germany and Uganda\u0026nbsp;[34,36]. However, our study identified a unique \"Body Fluid Exception\" where the site of work did not significantly influence adherence (p = 0.215). This suggests that disgust or the visible presence of fluids serves as a universal psychological trigger for hand hygiene that overrides the risk label of the ward. This innate behavioral driver represents a natural mechanism for IPC that could be leveraged in future training to cultivate more consistent hygiene habits across all clinical settings. We also demonstrated that healthcare professionals maintained consistently better compliance than support staff, which is consistent with meta analyses identifying clinicians and nurses as having greater adherence than nonclinical staff in Ghana, Uganda, and Somalia [35] [73,76,77]. The drop from 89.2 percent among professionals to a mere 16.7 percent among supporting staff in hand hygiene after body fluid exposure is s alarming. Given that supporting staff often handle infectious waste and contaminated linens, this 72.5 percent gap might represent a major institutional transmission pathway.\u003c/p\u003e\n\u003ch3\u003eLimitations of the study\u003c/h3\u003e\n\u003cp\u003eThis study has several limitations. First, adherence data were self-reported, introducing potential recall and social desirability bias. Participants may have overestimated their adherence due to social desirability, or underestimated due to recall lapses over the 14-day window. Second, the cross-sectional design precludes causal inference regarding factors associated with adherence or infection; we can only report associations, not causal pathways. Third, the study period (March 2020 to March 2021) coincided with evolving IPC guidelines and variable PPE supply chains, which may affect generalizability to current contexts where guidelines have stabilized and supply chains have improved. Fourth, the focus on infected HCWs only may introduce selection bias, as adherence patterns among uninfected HCWs may differ. Fifth, the single-country setting limits generalizability to other healthcare systems with different resource levels, training infrastructures, and cultural contexts. Sixth, the absence of objective adherence measures (e.g., direct observation) means reported practices may not fully reflect actual behavior. Seventh, the study did not assess whether HCWs received formal fit testing or seal-check training before the pandemic, which may have influenced practices during the study period.\u003c/p\u003e"},{"header":"Conclusions and Recommendations","content":"\u003cp\u003eThis study identifies significant protection gaps across multiple IPC domains, characterized by suboptimal N95 fit-testing, inconsistent pre-use seal checks, donning and doffing non-adherence, and reactive hand hygiene practice. These deficits form a distinct adherence gradient where better practices in designated settings prepared for high-consequence pathogens and among health professionals contrast with systemic vulnerabilities in general clinical environments and among support staff. With most infections occurring outside specialized units, primary occupational risk resides in general wards, driven by work site as an important determinant of adherence. Multivariable analysis confirms lower compliance odds in non-designated settings for both donning/doffing and pre-use seal-checks, with AGP involvement and IPC training acting as key positive predictors. To strengthen institutional resilience against high-consequence pathogens, we recommend standardization of IPC protocols across all clinical environments.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\n\u003cp\u003eAGP: Aerosol‑generating procedure; AIC: Akaike Information Criterion; AOR: Adjusted odds ratio; BIC: Bayesian Information Criterion; CDC: Centers for Disease Control and Prevention; CI: Confidence interval; COVID‑19: Coronavirus disease 2019; cOR: Crude odds ratio; EOC: Emergency Operating Center; ERC: Ethical Review Committee; GHR: General High‑Risk Areas; HCP: Healthcare professional; HCW: Healthcare worker; HPDA: High‑Pathogen Designated Areas; ICU: Intensive care unit; IPC: Infection prevention and control; IQR: Interquartile range; LRS: Low‑Risk Support Areas; MRCA: Medium‑Risk Clinical Areas; N95: N95 respirator; OSHA: Occupational Safety and Health Administration; PPE: Personal protective equipment; ROC‑AUC: Receiver operating characteristic \u0026ndash; area under the curve; RT‑PCR: Reverse transcription polymerase chain reaction; SD: Standard deviation; SR: Standardized residual; SS: Supporting staff; VIF: Variance inflation factor; WHO: World Health Organization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor Contributions:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZA: conceptualization, data curation, methodology, project administration, formal analysis, investigation, visualization, software, supervision, writing of the original draft, review and editing. TE: software, formal analysis, review and editing BR: conceptualization, supervision, project administration, Funding acquisition WW: conceptualization, supervision, project administration, review and editing SG: conceptualization, Writing \u0026ndash; original draft, review and editing AH and BE: data curation, writing original draft, review and editing MW,RWY and SY: conceptualization, supervision, resources BB: Software and formal analysis TF and ZC: conceptualization, project administration, funding acquisition, resources, supervision \u0026nbsp;BH: conceptualization, data curation, formal analysis, methodology, writing original draft, review and editing.\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;The authors received funding from the\u0026nbsp;Ministry\u0026nbsp;of\u0026nbsp;Health, Ethiopia.\u003c/p\u003e\n\u003cp\u003eInstitutional Review Board Statement: This research was reviewed and approved by the Ethical Review Committee (ERC) of the Addis Ababa Health Bureau (5204/227) in Ethiopia. Participants have given consent for their data to be used in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Informed consent was obtained from all subjects involved in the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e Supporting data for the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMaliszewska M, Mattoo A, Van der Mensbrugghe D. 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Accessed 27 Feb 2026\u003c/li\u003e\n \u003cli\u003eSelf-protection as a driver for hand hygiene among healthcare workers - PubMed [Internet]. [cited 2026 Feb 27]. https://pubmed.ncbi.nlm.nih.gov/19419325/. Accessed 27 Feb 2026\u003c/li\u003e\n \u003cli\u003eKamacooko O, Kitonsa J, Bahemuka UM, Kibengo FM, Wajja A, Basajja V, et al. Knowledge, Attitudes, and Practices Regarding COVID-19 among Healthcare Workers in Uganda: A Cross-Sectional Survey. Int J Environ Res Public Health. 2021;18:7004. https://doi.org/10.3390/ijerph18137004\u003c/li\u003e\n \u003cli\u003eJanay AI, Kilic B, Unal B. Healthcare workers\u0026rsquo; compliance with COVID-19 prevention and control measures at De Martino Hospital, Mogadishu, Somalia: a cross-sectional study. BMC Infect Dis. 2024;24:1046. https://doi.org/10.1186/s12879-024-09819-7\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-medicine-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tmah","sideBox":"Learn more about [Tropical Medicine and Health](https://tropmedhealth.biomedcentral.com/)","snPcode":"41182","submissionUrl":"https://submission.springernature.com/new-submission/41182/3","title":"Tropical Medicine and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Cross-Sectional Studies, Ethiopia, Guideline Adherence, Health Personnel, Infection Control, Occupational Exposure, Personal Protective Equipment","lastPublishedDoi":"10.21203/rs.3.rs-9297921/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9297921/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003cbr\u003e\n HCW (Health care workers) infections during outbreaks of high-consequence pathogens, from SARS-CoV-2 to Ethiopia's 2025–2026 Marburg outbreak, highlight persistent Infection Prevention and Control (IPC) gaps. Understanding the settings and determinants of adherence failure is essential to prepare for future outbreaks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\n This cross-sectional study of 1,152 SARS-CoV-2-infected HCWs in Addis Ababa (March 2020–March 2021) assessed IPC adherence among HCWs with face-to-face or body fluid/surface exposure from suspected/confirmed patients within 14 days pre-diagnosis. Clinical environments categorized via adapted OSHA pyramid: High-Pathogen Designated Areas (HPDA: isolation/treatment), General High-Risk Areas (GHR: ICUs/EDs/ORs), Medium-Risk Clinical Areas (MRCA: inpatient/outpatient), Low-Risk Support Areas (LRS: administrative/non-clinical). Adherence (PPE use/hand hygiene/donning \u0026amp; doffing) defined as practice in \u0026gt;50% of interactions. Chi-square/Fisher’s exact tests and Cramér’s V assessed bivariate associations. Multivariable logistic regression identified determinants for donning/doffing (HCWs with face-to-face contact, N=742) and N95 seal checks (N=350, N95 users).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\n Only 19.2% of infected HCWs worked in HPDA. Mask (85.7–97.7%) and glove (68.2–98.0%) adherence was high. Conversely, gowns, face shields/goggles, coveralls, head caps, N95, and shoe covers fell below 50%, except for face shields/goggles (52.8%) and N95 use during Aerosol Generating Procedures (AGPs) (66.5%). N95 seal‑check compliance was 34.9%; fit‑tested N95 use was 1.1%. Hand hygiene increased from 49.3% (before face‑to‑face) to 82.5% (after body fluid exposure). Per chi-square analysis, HPDA and health professionals had significantly better adherence than non-HPDA areas and support staff.\u003c/p\u003e\n\u003cp\u003eFor \u003cstrong\u003edonning/doffing\u003c/strong\u003e, odds were lower in non-HPDA areas (GHR aOR=0.12 [0.07–0.19]; MRCA aOR=0.08 [0.04–0.14]; LRS aOR=0.07 [0.03–0.17]). Adherence was higher with AGP involvement (aOR=3.61 [2.33–5.58]) and training (aOR=1.59 [1.05–2.41]), but lower among support staff (aOR=0.35 [0.14–0.88]). \u003cstrong\u003eN95 seal-check\u003c/strong\u003e odds were lower in non-HPDA areas (GHR aOR=0.53 [0.30–0.93]; MRCA aOR=0.33 [0.16–0.66]; LRS aOR=0.25 [0.08–0.77]). Positive predictors included communal living (aOR=2.37 [1.29–4.35]), IPC training (aOR=1.71 [1.03–2.83]), and AGP involvement (aOR=1.71 [1.05–2.80]), while age \u0026gt;30 (aOR=0.57 [0.34–0.98]) and stress (aOR=0.46 [0.28–0.77]) were negative determinants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003cbr\u003e\n This study underscores IPC gaps among HCWs especially in non-designated areas, where most infections occurred, highlighting the need for standardized IPC across all healthcare environments.\u003c/p\u003e","manuscriptTitle":"The Protection Gap: Infection prevention and control Adherence and Determinants among SARS-CoV-2 Infected Healthcare Workers in Ethiopia and Implications for Future High-Consequence Pathogen Outbreaks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-27 10:52:29","doi":"10.21203/rs.3.rs-9297921/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"313091722730705133385817734312202327027","date":"2026-05-19T09:30:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T13:19:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35612501616041560896620970712651416201","date":"2026-04-28T15:35:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"83837338532979287824559636407772911439","date":"2026-04-25T16:05:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325416388751408370749757080804393224428","date":"2026-04-25T13:48:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329718692140707575477593238081646885961","date":"2026-04-21T12:34:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-18T22:06:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T08:39:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T08:39:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Medicine and Health","date":"2026-04-02T04:18:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-medicine-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tmah","sideBox":"Learn more about [Tropical Medicine and Health](https://tropmedhealth.biomedcentral.com/)","snPcode":"41182","submissionUrl":"https://submission.springernature.com/new-submission/41182/3","title":"Tropical Medicine and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5783812-1aa0-449a-86a9-398a83b83c1f","owner":[],"postedDate":"April 27th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"313091722730705133385817734312202327027","date":"2026-05-19T09:30:27+00:00","index":75,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T13:19:56+00:00","index":48,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T10:52:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-27 10:52:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9297921","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9297921","identity":"rs-9297921","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}